OrganismsDiseasesChemicals and DrugsAnalytical, Diagnostic and Therapeutic Techniques and EquipmentPsychiatry and PsychologyPhenomena and ProcessesDisciplines and OccupationsAnthropology, Education, Sociology and Social PhenomenaTechnology, Industry, AgricultureInformation ScienceHealth CareGeographicals
DelawareUnited States Dept. of Health and Human ServicesWashingtonDurable Medical EquipmentUnited StatesState GovernmentMedicaidModels, StatisticalHepatitis, Infectious CanineDog DiseasesAlgorithmsDogsSoftwareData Interpretation, StatisticalStatistics as TopicPubMedPeriodicals as TopicPrognosisBooksGliomaPublishingAccess to InformationDengueNeurosciencesDevelopmental BiologyJournal Impact FactorDengue VirusTelemetryMovementAnimal MigrationGeographic Information SystemsEcologyAnimals, WildMarketingConsumer SatisfactionRestaurantsCommercePharmaciesMarketing of Health ServicesComputer SimulationModels, MolecularTosylarginine Methyl EsterBayes TheoremPasteurellosis, PneumonicGene LibraryLinear ModelsHypericumPsychological TheoryForecastingEnglandLikelihood FunctionsReproducibility of ResultsModels, Biological
Delaware
United States Dept. of Health and Human Services
A cabinet department in the Executive Branch of the United States Government concerned with administering those agencies and offices having programs pertaining to health and human services.
Washington
Durable Medical Equipment
Devices which are very resistant to wear and may be used over a long period of time. They include items such as wheelchairs, hospital beds, artificial limbs, etc.
United States
State Government
The level of governmental organization and function below that of the national or country-wide government.
Medicaid
Federal program, created by Public Law 89-97, Title XIX, a 1965 amendment to the Social Security Act, administered by the states, that provides health care benefits to indigent and medically indigent persons.
Models, Statistical
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Hepatitis, Infectious Canine
A contagious disease caused by canine adenovirus (ADENOVIRUSES, CANINE) infecting the LIVER, the EYE, the KIDNEY, and other organs in dogs, other canids, and bears. Symptoms include FEVER; EDEMA; VOMITING; and DIARRHEA.
Dog Diseases
Diseases of the domestic dog (Canis familiaris). This term does not include diseases of wild dogs, WOLVES; FOXES; and other Canidae for which the heading CARNIVORA is used.
Algorithms
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Dogs
The domestic dog, Canis familiaris, comprising about 400 breeds, of the carnivore family CANIDAE. They are worldwide in distribution and live in association with people. (Walker's Mammals of the World, 5th ed, p1065)
Software
Sequential operating programs and data which instruct the functioning of a digital computer.
Data Interpretation, Statistical
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
Statistics as Topic
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
PubMed
A bibliographic database that includes MEDLINE as its primary subset. It is produced by the National Center for Biotechnology Information (NCBI), part of the NATIONAL LIBRARY OF MEDICINE. PubMed, which is searchable through NLM's Web site, also includes access to additional citations to selected life sciences journals not in MEDLINE, and links to other resources such as the full-text of articles at participating publishers' Web sites, NCBI's molecular biology databases, and PubMed Central.
Periodicals as Topic
A publication issued at stated, more or less regular, intervals.
Prognosis
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
Books
Glioma
Benign and malignant central nervous system neoplasms derived from glial cells (i.e., astrocytes, oligodendrocytes, and ependymocytes). Astrocytes may give rise to astrocytomas (ASTROCYTOMA) or glioblastoma multiforme (see GLIOBLASTOMA). Oligodendrocytes give rise to oligodendrogliomas (OLIGODENDROGLIOMA) and ependymocytes may undergo transformation to become EPENDYMOMA; CHOROID PLEXUS NEOPLASMS; or colloid cysts of the third ventricle. (From Escourolle et al., Manual of Basic Neuropathology, 2nd ed, p21)
Publishing
"The business or profession of the commercial production and issuance of literature" (Webster's 3d). It includes the publisher, publication processes, editing and editors. Production may be by conventional printing methods or by electronic publishing.
Access to Information
Individual's rights to obtain and use information collected or generated by others.
Dengue
An acute febrile disease transmitted by the bite of AEDES mosquitoes infected with DENGUE VIRUS. It is self-limiting and characterized by fever, myalgia, headache, and rash. SEVERE DENGUE is a more virulent form of dengue.
Neurosciences
The scientific disciplines concerned with the embryology, anatomy, physiology, biochemistry, pharmacology, etc., of the nervous system.
Developmental Biology
The field of biology which deals with the process of the growth and differentiation of an organism.
Journal Impact Factor
A quantitative measure of the frequency on average with which articles in a journal have been cited in a given period of time.
Dengue Virus
A species of the genus FLAVIVIRUS which causes an acute febrile and sometimes hemorrhagic disease in man. Dengue is mosquito-borne and four serotypes are known.
Telemetry
Transmission of the readings of instruments to a remote location by means of wires, radio waves, or other means. (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed)
Movement
The act, process, or result of passing from one place or position to another. It differs from LOCOMOTION in that locomotion is restricted to the passing of the whole body from one place to another, while movement encompasses both locomotion but also a change of the position of the whole body or any of its parts. Movement may be used with reference to humans, vertebrate and invertebrate animals, and microorganisms. Differentiate also from MOTOR ACTIVITY, movement associated with behavior.
Animal Migration
Periodic movements of animals in response to seasonal changes or reproductive instinct. Hormonal changes are the trigger in at least some animals. Most migrations are made for reasons of climatic change, feeding, or breeding.
Geographic Information Systems
Computer systems capable of assembling, storing, manipulating, and displaying geographically referenced information, i.e. data identified according to their locations.
Ecology
The branch of science concerned with the interrelationship of organisms and their ENVIRONMENT, especially as manifested by natural cycles and rhythms, community development and structure, interactions between different kinds of organisms, geographic distributions, and population alterations. (Webster's, 3d ed)
Animals, Wild
Animals considered to be wild or feral or not adapted for domestic use. It does not include wild animals in zoos for which ANIMALS, ZOO is available.
Marketing
Activity involved in transfer of goods from producer to consumer or in the exchange of services.
Consumer Satisfaction
Customer satisfaction or dissatisfaction with a benefit or service received.
Restaurants
Commerce
The interchange of goods or commodities, especially on a large scale, between different countries or between populations within the same country. It includes trade (the buying, selling, or exchanging of commodities, whether wholesale or retail) and business (the purchase and sale of goods to make a profit). (From Random House Unabridged Dictionary, 2d ed, p411, p2005 & p283)
Pharmacies
Facilities for the preparation and dispensing of drugs.
Marketing of Health Services
Application of marketing principles and techniques to maximize the use of health care resources.
Computer Simulation
Computer-based representation of physical systems and phenomena such as chemical processes.
Models, Molecular
Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.
Tosylarginine Methyl Ester
Arginine derivative which is a substrate for many proteolytic enzymes. As a substrate for the esterase from the first component of complement, it inhibits the action of C(l) on C(4).
Bayes Theorem
A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.
Pasteurellosis, Pneumonic
Bovine respiratory disease found in animals that have been shipped or exposed to CATTLE recently transported. The major agent responsible for the disease is MANNHEIMIA HAEMOLYTICA and less commonly, PASTEURELLA MULTOCIDA or HAEMOPHILUS SOMNUS. All three agents are normal inhabitants of the bovine nasal pharyngeal mucosa but not the LUNG. They are considered opportunistic pathogens following STRESS, PHYSIOLOGICAL and/or a viral infection. The resulting bacterial fibrinous BRONCHOPNEUMONIA is often fatal.
Gene Library
A large collection of DNA fragments cloned (CLONING, MOLECULAR) from a given organism, tissue, organ, or cell type. It may contain complete genomic sequences (GENOMIC LIBRARY) or complementary DNA sequences, the latter being formed from messenger RNA and lacking intron sequences.
Linear Models
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
Hypericum
Genus of perennial plants in the family CLUSIACEAE (sometimes classified as Hypericaceae). Herbal and homeopathic preparations are used for depression, neuralgias, and a variety of other conditions. Hypericum contains flavonoids; GLYCOSIDES; mucilage, TANNINS; volatile oils (OILS, ESSENTIAL), hypericin and hyperforin.
Psychological Theory
Principles applied to the analysis and explanation of psychological or behavioral phenomena.
Forecasting
The prediction or projection of the nature of future problems or existing conditions based upon the extrapolation or interpretation of existing scientific data or by the application of scientific methodology.
England
Likelihood Functions
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
Reproducibility of Results
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
Models, Biological
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.

A computational screen for methylation guide snoRNAs in yeast. (1/16923)

Small nucleolar RNAs (snoRNAs) are required for ribose 2'-O-methylation of eukaryotic ribosomal RNA. Many of the genes for this snoRNA family have remained unidentified in Saccharomyces cerevisiae, despite the availability of a complete genome sequence. Probabilistic modeling methods akin to those used in speech recognition and computational linguistics were used to computationally screen the yeast genome and identify 22 methylation guide snoRNAs, snR50 to snR71. Gene disruptions and other experimental characterization confirmed their methylation guide function. In total, 51 of the 55 ribose methylated sites in yeast ribosomal RNA were assigned to 41 different guide snoRNAs.  (+info)

Influence of sampling on estimates of clustering and recent transmission of Mycobacterium tuberculosis derived from DNA fingerprinting techniques. (2/16923)

The availability of DNA fingerprinting techniques for Mycobacterium tuberculosis has led to attempts to estimate the extent of recent transmission in populations, using the assumption that groups of tuberculosis patients with identical isolates ("clusters") are likely to reflect recently acquired infections. It is never possible to include all cases of tuberculosis in a given population in a study, and the proportion of isolates found to be clustered will depend on the completeness of the sampling. Using stochastic simulation models based on real and hypothetical populations, the authors demonstrate the influence of incomplete sampling on the estimates of clustering obtained. The results show that as the sampling fraction increases, the proportion of isolates identified as clustered also increases and the variance of the estimated proportion clustered decreases. Cluster size is also important: the underestimation of clustering for any given sampling fraction is greater, and the variability in the results obtained is larger, for populations with small clusters than for those with the same number of individuals arranged in large clusters. A considerable amount of caution should be used in interpreting the results of studies on clustering of M. tuberculosis isolates, particularly when sampling fractions are small.  (+info)

Capture-recapture models including covariate effects. (3/16923)

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries.  (+info)

Sequence specificity, statistical potentials, and three-dimensional structure prediction with self-correcting distance geometry calculations of beta-sheet formation in proteins. (4/16923)

A statistical analysis of a representative data set of 169 known protein structures was used to analyze the specificity of residue interactions between spatial neighboring strands in beta-sheets. Pairwise potentials were derived from the frequency of residue pairs in nearest contact, second nearest and third nearest contacts across neighboring beta-strands compared to the expected frequency of residue pairs in a random model. A pseudo-energy function based on these statistical pairwise potentials recognized native beta-sheets among possible alternative pairings. The native pairing was found within the three lowest energies in 73% of the cases in the training data set and in 63% of beta-sheets in a test data set of 67 proteins, which were not part of the training set. The energy function was also used to detect tripeptides, which occur frequently in beta-sheets of native proteins. The majority of native partners of tripeptides were distributed in a low energy range. Self-correcting distance geometry (SECODG) calculations using distance constraints sets derived from possible low energy pairing of beta-strands uniquely identified the native pairing of the beta-sheet in pancreatic trypsin inhibitor (BPTI). These results will be useful for predicting the structure of proteins from their amino acid sequence as well as for the design of proteins containing beta-sheets.  (+info)

Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. (5/16923)

We examine the similarities and differences between two widely used knowledge-based potentials, which are expressed as contact matrices (consisting of 210 elements) that gives a scale for interaction energies between the naturally occurring amino acid residues. These are the Miyazawa-Jernigan contact interaction matrix M and the potential matrix S derived by Skolnick J et al., 1997, Protein Sci 6:676-688. Although the correlation between the two matrices is good, there is a relatively large dispersion between the elements. We show that when Thr is chosen as a reference solvent within the Miyazawa and Jernigan scheme, the dispersion between the M and S matrices is reduced. The resulting interaction matrix B gives hydrophobicities that are in very good agreement with experiment. The small dispersion between the S and B matrices, which arises due to differing reference states, is shown to have dramatic effect on the predicted native states of lattice models of proteins. These findings and other arguments are used to suggest that for reliable predictions of protein structures, pairwise additive potentials are not sufficient. We also establish that optimized protein sequences can tolerate relatively large random errors in the pair potentials. We conjecture that three body interaction may be needed to predict the folds of proteins in a reliable manner.  (+info)

Cloning, overexpression, purification, and physicochemical characterization of a cold shock protein homolog from the hyperthermophilic bacterium Thermotoga maritima. (6/16923)

Thermotoga maritima (Tm) expresses a 7 kDa monomeric protein whose 18 N-terminal amino acids show 81% identity to N-terminal sequences of cold shock proteins (Csps) from Bacillus caldolyticus and Bacillus stearothermophilus. There were only trace amounts of the protein in Thermotoga cells grown at 80 degrees C. Therefore, to perform physicochemical experiments, the gene was cloned in Escherichia coli. A DNA probe was produced by PCR from genomic Tm DNA with degenerated primers developed from the known N-terminus of TmCsp and the known C-terminus of CspB from Bacillus subtilis. Southern blot analysis of genomic Tm DNA allowed to produce a partial gene library, which was used as a template for PCRs with gene- and vector-specific primers to identify the complete DNA sequence. As reported for other csp genes, the 5' untranslated region of the mRNA was anomalously long; it contained the putative Shine-Dalgarno sequence. The coding part of the gene contained 198 bp, i.e., 66 amino acids. The sequence showed 61% identity to CspB from B. caldolyticus and high similarity to all other known Csps. Computer-based homology modeling allowed the conclusion that TmCsp represents a beta-barrel similar to CspB from B. subtilis and CspA from E. coli. As indicated by spectroscopic analysis, analytical gel permeation chromatography, and mass spectrometry, overexpression of the recombinant protein yielded authentic TmCsp with a molecular weight of 7,474 Da. This was in agreement with the results of analytical ultracentrifugation confirming the monomeric state of the protein. The temperature-induced equilibrium transition at 87 degrees C exceeds the maximum growth temperature of Tm and represents the maximal Tm-value reported for Csps so far.  (+info)

pKa calculations for class A beta-lactamases: influence of substrate binding. (7/16923)

Beta-Lactamases are responsible for bacterial resistance to beta-lactams and are thus of major clinical importance. However, the identity of the general base involved in their mechanism of action is still unclear. Two candidate residues, Glu166 and Lys73, have been proposed to fulfill this role. Previous studies support the proposal that Glu166 acts during the deacylation, but there is no consensus on the possible role of this residue in the acylation step. Recent experimental data and theoretical considerations indicate that Lys73 is protonated in the free beta-lactamases, showing that this residue is unlikely to act as a proton abstractor. On the other hand, it has been proposed that the pKa of Lys73 would be dramatically reduced upon substrate binding and would thus be able to act as a base. To check this hypothesis, we performed continuum electrostatic calculations for five wild-type and three beta-lactamase mutants to estimate the pKa of Lys73 in the presence of substrates, both in the Henri-Michaelis complex and in the tetrahedral intermediate. In all cases, the pKa of Lys73 was computed to be above 10, showing that it is unlikely to act as a proton abstractor, even when a beta-lactam substrate is bound in the enzyme active site. The pKa of Lys234 is also raised in the tetrahedral intermediate, thus confirming a probable role of this residue in the stabilization of the tetrahedral intermediate. The influence of the beta-lactam carboxylate on the pKa values of the active-site lysines is also discussed.  (+info)

Simplified methods for pKa and acid pH-dependent stability estimation in proteins: removing dielectric and counterion boundaries. (8/16923)

Much computational research aimed at understanding ionizable group interactions in proteins has focused on numerical solutions of the Poisson-Boltzmann (PB) equation, incorporating protein exclusion zones for solvent and counterions in a continuum model. Poor agreement with measured pKas and pH-dependent stabilities for a (protein, solvent) relative dielectric boundary of (4,80) has lead to the adoption of an intermediate (20,80) boundary. It is now shown that a simple Debye-Huckel (DH) calculation, removing both the low dielectric and counterion exclusion regions associated with protein, is equally effective in general pKa calculations. However, a broad-based discrepancy to measured pH-dependent stabilities is maintained in the absence of ionizable group interactions in the unfolded state. A simple model is introduced for these interactions, with a significantly improved match to experiment that suggests a potential utility in predicting and analyzing the acid pH-dependence of protein stability. The methods are applied to the relative pH-dependent stabilities of the pore-forming domains of colicins A and N. The results relate generally to the well-known preponderance of surface ionizable groups with solvent-mediated interactions. Although numerical PB solutions do not currently have a significant advantage for overall pKa estimations, development based on consideration of microscopic solvation energetics in tandem with the continuum model could combine the large deltapKas of a subset of ionizable groups with the overall robustness of the DH model.  (+info)

Multi-level visualisation using Gaussian process latent variable models - Research Explorer : Aston UniversityMulti-level visualisation using Gaussian process latent variable models - Research Explorer : Aston University
Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the Oil Flow dataset and a dataset of protein electrostatic potentials for the Major ...
https://research.aston.ac.uk/portal/en/researchoutput/multilevel-visualisation-using-gaussian-process-latent-variable-models
Doctoral Thesis: Latent Variable Models for Understanding User Behavior in Software Applications | MIT EECSDoctoral Thesis: Latent Variable Models for Understanding User Behavior in Software Applications | MIT EECS
Understanding user behavior in software applications is of significant interest to software developers and companies. By having a better understanding of the user needs and usage patterns, the developers can design a more efficient workflow, add new features, or even automate the users workflow. In this thesis, I propose novel latent variable models to understand, predict and eventually automate the user interaction with a software application. I start by analyzing users clicks using time series models; I introduce models and inference algorithms for time series segmentation which are scalable to large-scale user datasets. Next, using deep generative models (e.g. conditional variational autoencoder) and some related models, I introduce a framework for automating the user interaction with a software application. I focus on photo enhancement applications, but this framework can be applied to any domain where segmentation, prediction and personalization is valuable. Finally, by combining ...
http://www.eecs.mit.edu/news-events/calendar/events/doctoral-thesis-latent-variable-models-understanding-user-behavior
Items where Subject is 5. Quantitative Data Handling and Data Analysis | 5.10 Latent Variable Models | 5.10.7 Confirmatory...Items where Subject is 5. Quantitative Data Handling and Data Analysis | 5.10 Latent Variable Models | 5.10.7 Confirmatory...
Items where Subject is 5. Quantitative Data Handling and Data Analysis , 5.10 Latent Variable Models , 5.10.7 Confirmatory factor analysis ...
http://eprints.ncrm.ac.uk/view/subjects/05=5F10=5F07.html
Analysis of HIV/AIDS DRG in Portugal: A hierarchical finite mixture model<...Analysis of HIV/AIDS DRG in Portugal: A hierarchical finite mixture model<...
TY - JOUR. T1 - Analysis of HIV/AIDS DRG in Portugal. T2 - A hierarchical finite mixture model. AU - Dias, Sara Simões. AU - Andreozzi, Valeska. AU - Martins, Rosário O.. PY - 2013. Y1 - 2013. N2 - Inpatient length of stay (LOS) is an important measure of hospital activity, but its empirical distribution is often positively skewed, representing a challenge for statistical analysis. Taking this feature into account, we seek to identify factors that are associated with HIV/AIDS through a hierarchical finite mixture model. A mixture of normal components is applied to adult HIV/AIDS diagnosis-related group data (DRG) from 2008. The model accounts for the demographic and clinical characteristics of the patients, as well the inherent correlation of patients clustered within hospitals. In the present research, a normal mixture distribution was fitted to the logarithm of LOS and it was found that a model with two-components had the best fit, resulting in two subgroups of LOS: a short-stay subgroup and ...
https://novaresearch.unl.pt/en/publications/analysis-of-hivaids-drg-in-portugal-a-hierarchical-finite-mixture-2
Statistics - Exponential distribution - TutorialspointStatistics - Exponential distribution - Tutorialspoint
Statistics - Exponential distribution - Exponential distribution or negative exponential distribution represents a probability distribution to describe the time between events in a Poisson process. In
https://www.tutorialspoint.com/statistics/exponential_distribution.htm
Finite mixtures of generalized linear regression models by Bettina Grun and Friedrich LeischFinite mixtures of generalized linear regression models by Bettina Grun and Friedrich Leisch
Finite mixture models have now been used for more than hundred years (Newcomb (1886), Pearson (1894)). They are a very popular statistical modeling technique given that they constitute a flexible and-easily extensible model class for (1) approximating general distribution functions in a semi-parametric way and (2) accounting for unobserved heterogeneity. The number of applications has tremendously increased in the last decades as model estimation in a frequentist as well as a Bayesian framework has become feasible with the nowadays easily available computing power. The simplest finite mixture models are finite mixtures of distributions which are used for model-based clustering. In this case the model is given by a convex combination of a finite number of different distributions where each of the distributions is referred to as component. More complicated mixtures have been developed by inserting different kinds of models for each component. An obvious extension is to estimate a generalized linear model
https://ro.uow.edu.au/commpapers/2056/
Local influence analysis of multivariate probit latent variable modelsLocal influence analysis of multivariate probit latent variable models
Downloadable (with restrictions)! The multivariate probit model is very useful for analyzing correlated multivariate dichotomous data. Recently, this model has been generalized with a confirmatory factor analysis structure for accommodating more general covariance structure, and it is called the MPCFA model. The main purpose of this paper is to consider local influence analysis, which is a well-recognized important step of data analysis beyond the maximum likelihood estimation, of the MPCFA model. As the observed-data likelihood associated with the MPCFA model is intractable, the famous Cooks approach cannot be applied to achieve local influence measures. Hence, the local influence measures are developed via Zhu and Lees [Local influence for incomplete data model, J. Roy. Statist. Soc. Ser. B 63 (2001) 111-126.] approach that is closely related to the EM algorithm. The diagnostic measures are derived from the conformal normal curvature of an appropriate function. The building blocks are computed via a
https://ideas.repec.org/a/eee/jmvana/v97y2006i8p1783-1798.html
Browse - Oxford ScholarshipBrowse - Oxford Scholarship
At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic ... More. At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic models whose graphical components denote conditional independence structures between random variables. The probabilistic framework makes it possible to deal with data uncertainty while the conditional independence assumption helps process high dimensional and complex data. Examples of probabilistic graphical models are Bayesian networks and Markov random fields, which represent two of the most popular classes of such models. With the rapid advancements of high-throughput technologies and the ever decreasing costs of these next generation technologies, a fast-growing volume of biological data of ...
http://oxfordscholarship.com/browse?t_0=OSO%3Amaths&t_1=OSO%3Amatbios
Likelihood ratio test of model specification - MATLAB lratiotestLikelihood ratio test of model specification - MATLAB lratiotest
This MATLAB function returns a logical value (h) with the rejection decision from conducting a likelihood ratio test of model specification.
http://www.mathworks.com/help/econ/lratiotest.html?requestedDomain=true&nocookie=true
Estimating Monetary Policy Reaction Functions Using Quantile RegressionsEstimating Monetary Policy Reaction Functions Using Quantile Regressions
Downloadable! Monetary policy rule parameters are usually estimated at the mean of the interest rate distribution conditional on inflation and an output gap. This is an incomplete description of monetary policy reactions when the parameters are not uniform over the conditional distribution of the interest rate. I use quantile regressions to estimate parameters over the whole conditional distribution of the Federal Funds Rate. Inverse quantile regressions are applied to deal with endogeneity. Realtime data of inflation forecasts and the output gap are used. I find significant and systematic variations of parameters over the conditional distribution of the interest rate.
https://ideas.repec.org/p/pra/mprapa/23857.html
Probit model - WikipediaProbit model - Wikipedia
In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word is a portmanteau, coming from probability + unit.[1] The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories; moreover, classifying observations based on their predicted probabilities is a type of binary classification model. A probit model is a popular specification for an ordinal[2] or a binary response model. As such it treats the same set of problems as does logistic regression using similar techniques. The probit model, which employs a probit link function, is most often estimated using the standard maximum likelihood procedure, such an estimation being called a probit regression. ...
https://en.wikipedia.org/wiki/Probit_model
A Goodness-of-Fit Test for Multivariate Normal Distribution Using Modified Squared Distance | Korea ScienceA Goodness-of-Fit Test for Multivariate Normal Distribution Using Modified Squared Distance | Korea Science
A Goodness-of-Fit Test for Multivariate Normal Distribution Using Modified Squared Distance - Multivariate normal distribution;goodness-of-fit test;empirical distribution function;modified squared distance;
http://www.koreascience.or.kr/article/ArticleFullRecord.jsp?cn=GCGHC8_2012_v19n4_607
Adaptive Piecewise Linear Bits Estimation Model for MPEG Based Video Coding<...Adaptive Piecewise Linear Bits Estimation Model for MPEG Based Video Coding<...
TY - JOUR. T1 - Adaptive Piecewise Linear Bits Estimation Model for MPEG Based Video Coding. AU - Cheng, Jia Bao. AU - Hang, Hsueh-Ming. PY - 1997/3. Y1 - 1997/3. N2 - In many video compression applications, it is essential to control precisely the bit rate produced by the encoder. One critical element in a bits/buffer control algorithm is the bits model that predicts the number of compressed bits when a certain quantization stepsize is used. In this paper, we propose an adaptive piecewise linear bits estimation model with a tree structure. Each node in the tree is associated with a linear relationship between the compressed bits and the activity measure divided by stepsize. The parameters in this relationship are adjusted by the least mean squares algorithm. The effectiveness of this algorithm is demonstrated by an example of digital VCR application. Simulation results indicate that this bits model has a fast adaptation speed even during scene changes. Compared to the bits model derived from ...
https://scholar.nctu.edu.tw/en/publications/adaptive-piecewise-linear-bits-estimation-model-for-mpeg-based-vi-2
Temporal Dependence in Limited Dependent Variable Models: Theoretical by Vassilis A. HajivassiliouTemporal Dependence in Limited Dependent Variable Models: Theoretical by Vassilis A. Hajivassiliou
This paper analyzes the consistency properties of classical estimators for limited dependent variables models, under conditions of serial correlation in the unobservables. A unified method of proof is used to show that for certain cases (e.g., Probit, Tobit and Normal Switching Regimes models, which are normality-based) estimators that neglect particular types of serial dependence (specifically, corresponding to the class of
https://elischolar.library.yale.edu/cowles-discussion-paper-series/1046/
Chi-square goodness-of-fit testChi-square goodness-of-fit test
Theory and lecture notes of Chi-square goodness-of-fit test all along with the key concepts of chi-square goodness-of-fit test, Interpreting the Claim. Tutorsglobe offers homework help, assignment help and tutors assistance on Chi-square goodness-of-fit test.
http://www.tutorsglobe.com/homework-help/statistics/chi-square-goodness-of-fit-test-74206.aspx
Looking for Multiple Modes: Are Galaxies Clustered? :: SAS/STAT(R) 14.1 Users GuideLooking for Multiple Modes: Are Galaxies Clustered? :: SAS/STAT(R) 14.1 Users Guide
Analysis of potentially multimodal data is a natural application of finite mixture models. In this case, the modeling is complicated by the question of the variance for each of the components. Using identical variances for each component could obscure underlying structure, but the additional flexibility granted by component-specific variances might introduce spurious features. You can use PROC HPFMM to prepare analyses for equal and unequal variances and use one of the available fit statistics to compare the resulting models. You can use the model selection facility to explore models with varying numbers of mixture components-say, from three to seven as investigated in Roeder (1990). The following statements select the best unequal-variance model using Akaikes information criterion (AIC), which has a built-in penalty for model complexity: ...
http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/statug_hpfmm_gettingstarted03.htm
generalized linear model - Do test scores really follow a normal distribution? - Cross Validatedgeneralized linear model - Do test scores really follow a normal distribution? - Cross Validated
Yes, but more than that -- they tend to be heavily right skew and the variability tends to increase when the mean gets larger.. Heres an example of a claim-size distribution for vehicle claims:. https://ars.els-cdn.com/content/image/1-s2.0-S0167668715303358-gr5.jpg. (Fig 5 from Garrido, Genest & Schulz (2016) Generalized linear models for dependent frequency and severity of insurance claims, Insurance: Mathematics and Economics, Vol 70, Sept., p205-215. https://www.sciencedirect.com/science/article/pii/S0167668715303358). This shows a typical right-skew and heavy right tail. However we must be very careful because this is a marginal distribution, and we are writing a model for the conditional distribution, which will typically be much less skew (the marginal distribution we look at if we just do a histogram of claim sizes being a mixture of these conditional distributions). Nevertheless it is typically the case that if we look at the claim size in subgroups of the predictors (perhaps ...
https://stats.stackexchange.com/questions/376512/do-test-scores-really-follow-a-normal-distribution/439682
probability - Bayesian Network: Calculate probabillity of child node given all probabillity tables - Cross Validatedprobability - Bayesian Network: Calculate probabillity of child node given all probabillity tables - Cross Validated
As you have described it, there is not enough information to know how to conditional probability of the child from the parents. You have described that you have the marginal probabilities of each node; this tells you nothing about the relationship between nodes. For example, if you observed that 50% of people in a study take a drug (and the others take placebo), and then you later note that 20% of the people in the study had an adverse outcome, you do not have enough information to know how the probability of the child (adverse outcome) depends on the probability of the parent (taking the drug). You need to know the joint distribution of the parents and child to learn the conditional distribution. The joint distribution requires that you know the probability of the combination of all possible values for the parents and the children. From the joint distribution, you can use the definition of conditional probability to find the conditional distribution of the child on the parents.. ...
https://stats.stackexchange.com/questions/352545/bayesian-network-calculate-probabillity-of-child-node-given-all-probabillity-ta
Mixture models for analysis of melting temperature data. - Nuffield Department of Obstetrics and GynaecologyMixture models for analysis of melting temperature data. - Nuffield Department of Obstetrics and Gynaecology
BACKGROUND: In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data. RESULTS: Mixture model analysis was applied to Tm data. Models were selected based on Akaikes information criterion. Mixture model analysis correctly identified categories in Tm data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated. CONCLUSION: Mixture model analysis of Tm data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows Tm data
https://www.wrh.ox.ac.uk/publications/344391
Dominik Janzing | Empirical Inference - Max Planck Institute for Intelligent SystemsDominik Janzing | Empirical Inference - Max Planck Institute for Intelligent Systems
div class=share-contianer, ,ul class=social-icons social-icons-color custom-share-buttons, ,li, ,a href=https://www.facebook.com/sharer/sharer.php?u=https://ei.is.tuebingen.mpg.de/~janzing/janzingmzlzdss2012 onclick=popupCenter($(this).attr(href), , 580, 470); return false; class=popup social_facebook,,/a, ,!-- ,a href=https://www.facebook.com/sharer/sharer.php?s=100&p[title]=While conventional approaches to causal inference are mainly based on conditional (in)dependences, recent methods also account for the shape of (conditional) distributions. The idea is that the causal hypothesis X causes Y imposes that the marginal distribution PX and the conditional distribution PY,X represent independent mechanisms of nature. Recently it has been postulated that the shortest description of the joint distribution PX,Y should therefore be given by separate descriptions of PX and PY,X. Since description length in the sense of Kolmogorov complexity is uncomputable, practical implementations ...
https://ei.is.tuebingen.mpg.de/~janzing
Bayesian Optimal Designs for Efficient Estimation of the Optimum Point with Generalised Linear Models | ScholarBank@NUSBayesian Optimal Designs for Efficient Estimation of the Optimum Point with Generalised Linear Models | [email protected]
Guilin Li, Szu Hui Ng, Matthias Hwai-yong Tan (2018-11-20). Bayesian Optimal Designs for Efficient Estimation of the Optimum Point with Generalised Linear Models. Quality Technology & Quantitative Management 17 (01) : 89-107. [email protected] Repository. https://doi.org/10.1080/16843703.2018. ...
https://scholarbank.nus.edu.sg/handle/10635/163694?mode=full
Random coefficient models - Encyclopedia of MathematicsRandom coefficient models - Encyclopedia of Mathematics
This Appendix describes a method for fitting a random coefficient model by maximum likelihood. We prefer to use the scaled variance matrices $\bW_j = \sigma^{-2} \bV_j$ and $\bOmega = \sigma^{-2} \bSigma_{\rm B\,}$, so that $\bW_j = \bI_{n_d} + \bZ_j \bOmega \bZ_j \tra$ does not depend on $\sigma^2$. The log-likelihood in \ref{Fisc1} is equal to $$ \label{Fisc2} l \left (\bbeta, \sigma^2, \bOmega \right ) \,=\, -\frac{1}{2} \sum_{j=1}^m \left [ n \log \left ( \sigma^2 \right ) + \log \left \{ \det \left ( \bW_j \right ) \right \} \,+\, \frac{1}{\sigma^2} \, \be_j \tra \bW_j^{-1} \be_j \right ] \,. \tag{6}$$ where $\be_j = \by_j - \bX_j \bbeta$ is the vector of residuals for cluster $j$. We have the following closed-form expressions for the inverse and determinant of $\bW_{j\,}$: \begin{eqnarray} \label{FiscI} \bW_j^{-1} &=& \bI_{n_d} - \bZ_j \bOmega \bG_j^{-1} \bZ_j \tra \nonumber \\ \det \left ( \bW_j \right ) &=& \sigma^{2n_d} \det \left ( \bG_j \right ) \,, \end{eqnarray} where $\bG_j = ...
https://encyclopediaofmath.org/wiki/Random_coefficient_models
Comparing linear probability model coefficients across groups - Professorer og lektorerComparing linear probability model coefficients across groups - Professorer og lektorer
This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more of the following three components: outcome truncation, scale parameters and distributional shape of the predictor variable. These results point to limitations in using linear probability model coefficients for group comparisons. We also provide Monte Carlo simulations and real examples to illustrate these limitations, and we suggest a restricted approach to using linear probability model coefficients in group comparisons ...
https://www.soc.ku.dk/ansatte/professorer/?pure=da%2Fpublications%2Fcomparing-linear-probability-model-coefficients-across-groups
R] conservative robust estimation in (nonlinear) mixed modelsR] conservative robust estimation in (nonlinear) mixed models
Conservative robust estimation methods do not appear to be currently available in the standard mixed model methods for R, where by conservative robust estimation I mean methods which work almost as well as the methods based on assumptions of normality when the assumption of normality *IS* satisfied. We are considering adding such a conservative robust estimation option for the random effects to our AD Model Builder mixed model package, glmmADMB, for R, and perhaps extending it to do robust estimation for linear mixed models at the same time. An obvious candidate is to assume something like a mixture of normals. I have tested this in a simple linear mixed model using 5% contamination with a normal with 3 times the standard deviation, which seems to be a common assumption. Simulation results indicate that when the random effects are normally distributed this estimator is about 3% less efficient, while when the random effects are contaminated with 5% outliers the estimator is about 23% more ...
https://stat.ethz.ch/pipermail/r-help/2006-March/102194.html
Nonparametric and Parametric Survival Analysis of Censored Data with Possible Violation of Method Assumptions, UNCG NC DOCKS ...Nonparametric and Parametric Survival Analysis of Censored Data with Possible Violation of Method Assumptions, UNCG NC DOCKS ...
Abstract: Estimating survival functions has interested statisticians for numerous years. A survival function gives information on the probability of a time-to-event of interest. Research in the area of survival analysis has increased greatly over the last several decades because of its large usage in areas related to biostatistics and the pharmaceutical industry. Among the methods which estimate the survival function, several are widely used and available in popular statistical software programs. One purpose of this research is to compare the efficiency between competing estimators of the survival function. Results are given for simulations which use nonparametric and parametric estimation methods on censored data. The simulated data sets have right-, left-, or interval-censored time points. Comparisons are done on various types of data to see which survival function estimation methods are more suitable. We consider scenarios where distributional assumptions or censoring type assumptions are ...
http://libres.uncg.edu/ir/uncg/listing.aspx?id=372
Most recent papers with the keyword nlme | Read by QxMDMost recent papers with the keyword nlme | Read by QxMD
Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework ...
https://www.readbyqxmd.com/keyword/53229
Research Labs and Institutes | Johns Hopkins MedicineResearch Labs and Institutes | Johns Hopkins Medicine
The primary area of statistical expertise in the Qian-Li Xue Lab is the development and application of statistical methods for: (1) handling the truncation of information on underlying or unobservable outcomes (e.g., disability) as a result of screening, (2) missing data, including outcome (e.g., frailty) censoring by a competing risk (e.g., mortality) and (3) trajectory analysis of multivariate outcomes. Other areas of methodologic research interests include multivariate, latent variable models. In Womens Health and Aging Studies, we have closely collaborated with scientific investigators on the design and analysis of longitudinal data relating biomarkers of inflammation, hormonal dysregulation and micronutrient deficiencies to the development and progression of frailty and disability, as well as characterizing the natural history of change in cognitive and physical function over time.. Research Areas: epidemiology, disabilities, longitudinal data, hormonal dysregulation, womens health, ...
https://www.hopkinsmedicine.org/research/research_lab_results.html?query=outcomes&pagesize=10&page=6&firstLetter=
The Chi-Square Goodness-of-Fit Test Research Paper - 501 WordsThe Chi-Square Goodness-of-Fit Test Research Paper - 501 Words
THE CHI-SQUARE GOODNESS-OF-FIT TEST The chi-square goodness-of-fit test is used to analyze probabilities of multinomial distribution trials along a single
http://www.studymode.com/essays/The-Chi-Square-Goodness-Of-Fit-Test-1781711.html
Discovery of Latent Factors in High-dimensional Data Via Spectral Methods | UMD Department of Computer ScienceDiscovery of Latent Factors in High-dimensional Data Via Spectral Methods | UMD Department of Computer Science
Latent variable models have a broad set of applications in domains such as social networks, natural language processing, computer vision and computational biology. Training them on a large scale is challenging due to non-convexity of the objective function. We propose a unified framework that exploits tensor algebraic constraints of the (low order) moments of the models.
http://www.cs.umd.edu/event/2017/10/discovery-latent-factors-high-dimensional-data-spectral-methods?width=500&height=600&title=Discovery%20of%20Latent%20Factors%20in%20High-dimensional%20Data%20Via%20Spectral%20Methods
Universidad de los Andes - MATE1505Universidad de los Andes - MATE1505
The objective of this course is to familiarize students with the basic concepts of probability and the most common distributions. This knowledge will be useful not only for future courses of Statistics of Stochastic Processes, but is also directly applicable in many situations where chance or randomness prevail. Combinatory Methods. Binomial coefficients. Sample Spaces. Probability, rules. Conditional probability, independence. Bayes Theorem. Probability distributions. . Continuous random variables, density functions. Multivariate distributions. Marginal distributions. Conditional distributions. Expected value. Moments, Chebyshevs Theorem. Moment generating functions. Product moments. Comb moments. Linear moments, conditional expectation. Uniform, Bernoulli, Binomial. Negative binomial, geometric, hyper-geometric. Poisson. Multinomial, multivariate hyper-geometric. Uniform, gamma, exponential, j-I squared. Beta distribution. Normal distribution. Normal to binomial approximation. Normal ...
https://catalogo.uniandes.edu.co/en/2018/Catalogo/Cursos/MATE/1000/MATE-1505
Comparing fMRI Activity Maps from GLM and CCA at the Same Significance Level by Fast Random Permutation Tests on the GPUComparing fMRI Activity Maps from GLM and CCA at the Same Significance Level by Fast Random Permutation Tests on the GPU
Parametric statistical methods are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it isassumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. In this work it is shown how the computational power of the Graphics Processing Unit (GPU) can be used to speedup non-parametric tests, such as random permutation tests. With random permutation tests it is possible to calculate significance thresholds for any test statistics. As an example, fMRI activity maps from the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level.. ...
http://liu.diva-portal.org/smash/record.jsf?pid=diva2:402372
The Hosmer-Lemeshow Goodness-of-Fit Test :: SAS/STAT(R) 13.2 Users GuideThe Hosmer-Lemeshow Goodness-of-Fit Test :: SAS/STAT(R) 13.2 Users Guide
Sufficient replication within subpopulations is required to make the Pearson and deviance goodness-of-fit tests valid. When there are one or more continuous predictors in the model, the data are often too sparse to use these statistics. Hosmer and Lemeshow (2000) proposed a statistic that they show, through simulation, is distributed as chi-square when there is no replication in any of the subpopulations. This test is available only for binary response models. First, the observations are sorted in increasing order of their estimated event probability. The event is the response level specified in the response variable option EVENT= , or the response level that is not specified in the REF= option, or, if neither of these options was specified, then the event is the response level identified in the Response Profiles table as Ordered Value 1. The observations are then divided into approximately 10 groups according to the following scheme. Let N be the total number of subjects. Let M be the ...
http://support.sas.com/documentation/cdl/en/statug/67523/HTML/default/statug_logistic_details32.htm
A scaled difference chi-square test statistic for moment structure analysisA scaled difference chi-square test statistic for moment structure analysis
A family of scaling corrections aimed to improve the chi-square approximation of goodness-of-fit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentlers (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a test of the restrictions that a null model say ${\cal M}_0$ implies on a less restricted one ${\cal M}_1$. If $T_0$ and $T_1$ denote the goodness-of-fit test statistics associated to ${\cal M}_0$ and ${\cal M}_1$, respectively, then typically the difference $T_d = T_0 - T_1$ is used as a chi-square test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the models ${\cal M}_0$ and ${\cal M}_1$. As in the case of the goodness-of-fit test, it is of interest to scale the statistic $T_d$ in order to improve its chi-square approximation in ...
http://www.recercat.cat/handle/2072/558
Bootstrap Confidence Intervals and Coverage Probabilities of Regression Parameter Est . . . by Matthew Hall and Matthew S....Bootstrap Confidence Intervals and Coverage Probabilities of Regression Parameter Est . . . by Matthew Hall and Matthew S....
Mayo and Gray introduced the leverage residual-weighted elemental (LRWE) classification of regression estimators and a new method of estimation called trimmed elemental estimation (TEE), showing the efficiency and robustness of TEE point estimates. Using bootstrap methods, properties of various trimmed elemental estimator interval estimates to allow for inference are examined and estimates with ordinary least squares (OLS) and least sum of absolute values (LAV) are compared. Confidence intervals and coverage probabilities for the estimators using a variety of error distributions, sample sizes, and number of parameters are examined. To reduce computational intensity, randomly selecting elemental subsets to calculate the parameter estimates were investigated. For the distributions considered, randomly selecting 50% of the elemental regressions led to highly accurate estimates.
http://digitalcommons.wayne.edu/jmasm/vol7/iss2/17/
Asymptotic Procedures for a Change-Point Analysis of Random Field - Kölner UniversitätsPublikationsServerAsymptotic Procedures for a Change-Point Analysis of Random Field - Kölner UniversitätsPublikationsServer
The aim of this thesis is to extend some methods of change-point analysis, where classically, measurements in time are examined for structural breaks, to random field data which is observed over a grid of points in multidimensional space. The thesis is concerned with the a posteriori detection and estimation of changes in the marginal distribution of such random field data. In particular, the focus lies on constructing nonparametric asymptotic procedures which take the possible stochastic dependence into account. In order to avoid having to restrict the results to specific distributional assumptions, the tests and estimators considered here use a nonparametric approach where the inference is justified by the asymptotic behavior of the considered procedures (i.e. their behavior as the sample size goes towards infinity). This behavior can often be derived from functional central limit theorems which make it possible to write the limit variables of the statistics as functionals of Wiener processes, ...
http://kups.ub.uni-koeln.de/6945/
Useless Algebra, Inefficient Computation, and Opaque Model Specifications - Statistical Modeling, Causal Inference, and Social...Useless Algebra, Inefficient Computation, and Opaque Model Specifications - Statistical Modeling, Causal Inference, and Social...
I am always struck by this same issue. Heres what I think is going on:. 1. What goes in a paper is up to the author. If the author struggled with a step or found it a bit tricky to think about themselves, then the struggle goes into the paper. Even if it might be obvious to someone with more experience in a field. I was just reading a paper with a very detailed exposition of EM for a latent logistic regression problem with conditional probability derivations, etc. (JMLR paper Learning from Crowds by Raykar et al., which is an awesome paper, even if it suffers from this flaw and number 3.). 2. What goes in a paper is up to editors. If the editors dont understand something, theyll ask for details, even if they should be obvious to the entire field. This is agreeing with Roberts point, I think. Editors like to see the author sweat, because of some kind of no-pain, no-gain esthetic that seems to permeate academic journal publishing. Its so hard to boil something down, then when you do, you get ...
http://andrewgelman.com/2014/06/28/useless-algebra-inefficient-computation-opaque-models/
64b300864b3008
By. G. Jogesh Babu and C.R. Rao, The Pennsylvania State University, University Park, USA. SUMMARY. Several nonparametric goodness-of-fit tests are based on the empirical distribution function. In the presence of nuisance parameters, the tests are generally constructed by first estimating these nuisance parameters. In such a case, it is well known that critical values shift, and the asymptotic null distribution of the test statistic may depend in a complex way on the unknown parameters. In this paper we use bootstrap methods to estimate the null distribution. We shall consider both parametric and nonparametric bootstrap methods. We shall first demonstrate that, under very general conditions, the process obtained by subtracting the population distribution function with estimated parameters from the empirical distribution has the same weak limit as the corresponding bootstrap version. Of course in the nonparametric bootstrap case a bias correction is needed. This result is used to show that the ...
http://sankhya.isical.ac.in/search/66_1/2004005.html
Research - Guidon FenigResearch - Guidon Fenig
We evaluate data on choices made from Convex Time Budgets (CTB) in Andreoni and Sprenger (2012a) and Augenblick et al (2015), two influential studies that proposed and applied this experimental technique. We use the Weak Axiom of Revealed Preference (WARP) to test for external consistency relative to pairwise choice, and demand, wealth and impatience monotonicity to test for internal consistency. We find that choices made by subjects in the original Andreoni and Sprenger (2012a) paper violate WARP frequently; violations of all three internal measures of monotonicity are concentrated in subjects who take advantage of the novel feature of CTB by making interior choices. Wealth monotonicity violations are more prevalent and pronounced than either demand or impatience monotonicity violations. We substantiate the importance of our desiderata of choice consistency in examining effort allocation choices made in Augenblick et al (2015), where we find considerably more demand monotonicity violations, as ...
https://www.guidonfenig.com/research.html
Testing Regression Specifications (Advanced Data Analysis from an Elementary Point of View)Testing Regression Specifications (Advanced Data Analysis from an Elementary Point of View)
Non-parametric smoothers can be used to test parametric models. Forms of tests: differences in in-sample performance; differences in generalization performance; whether the parametric models residuals have expectation zero everywhere. Constructing a test statistic based on in-sample performance. Using bootstrapping from the parametric model to find the null distribution of the test statistic. An example where the parametric model is correctly specified, and one where it is not. Cautions on the interpretation of goodness-of-fit tests. Why use parametric models at all? Answers: speed of convergence when correctly specified; and the scientific interpretation of parameters, if the model actually comes from a scientific theory. Mis-specified parametric models can predict better, at small sample sizes, than either correctly-specified parametric models or non-parametric smoothers, because of their favorable bias-variance characteristics; an example. Reading: Notes, chapter 10 Advanced Data Analysis ...
http://bactra.org/notebooks/877.html
What is the risk estimation model for SSL VPN implementation?What is the risk estimation model for SSL VPN implementation?
Get network security expert advice on VPN risk analysis and learn the risk estimation model necessary to assess SSL VPN implementation.
http://searchsecurity.techtarget.com/answer/What-is-the-risk-estimation-model-for-SSL-VPN-implementation
KernelMixtureDistribution-Wolfram Language DocumentationKernelMixtureDistribution-Wolfram Language Documentation
KernelMixtureDistribution[{x1, x2, ...}] represents a kernel mixture distribution based on the data values xi. KernelMixtureDistribution[{{x1, y1, ...}, {x2, y2, ...}, ...}] represents a multivariate kernel mixture distribution based on data values {xi, yi, ...}. KernelMixtureDistribution[..., bw] represents a kernel mixture distribution with bandwidth bw. KernelMixtureDistribution[..., bw, ker] represents a kernel mixture distribution with bandwidth bw and smoothing kernel ker.
http://reference.wolfram.com/language/ref/KernelMixtureDistribution.html
Mixture distribution latent state-trait analysis: Basic ideas and applications.Mixture distribution latent state-trait analysis: Basic ideas and applications.
Courvoisier, D. S., Eid, M., & Nussbeck, F. W. (2007). Mixture distribution latent state-trait analysis: Basic ideas and applications. Psychological Methods, 12(1), 80-104. doi:10.1037/1082-989X.12.1. ...
https://pub.uni-bielefeld.de/publication/2557204
Accelerated Development and Improved Process Operations Using Advanced Multivariate Latent Variable Modeling | AIChEAccelerated Development and Improved Process Operations Using Advanced Multivariate Latent Variable Modeling | AIChE
This talk will focus on the use of advanced multivariate latent variable models to aid the accelerated development of the product and the process, as
https://www.aiche.org/academy/videos/conference-presentations/accelerated-development-and-improved-process-operations-using-advanced-multivariate-latent-variable
NetStim Fractional Randomness: Mathematical Notation? - www.neuron.yale.eduNetStim Fractional Randomness: Mathematical Notation? - www.neuron.yale.edu
Right, so my understanding of how fractional randomness is implemented is that for each interspike interval, the interval is dependent on the values set for s.interval and s.noise (assuming s = new NetStim(x)). Specifically, the magnitude of the value of s.noise (from 0 to 1) will control the proportion by which the interval is dependent on s.interval vs random values sampled from a negative exponential distribution. For example if s.noise = 0.2, the actual interval will be 0.8*s.interval + a random duration sampled from a negative exponential distribution with a mean duration of 0.2*s.interval. I am mostly unsure about how this negative exponential distribution (i.e. X) is represented mathematically. Honestly, from my limited understanding I wouldve written it as follows ...
https://www.neuron.yale.edu/phpBB/viewtopic.php?p=16385
NIOSHTIC-2 Publications Search - 20022894 - Miscellanea probabilistic model for two dependent circular variables.NIOSHTIC-2 Publications Search - 20022894 - Miscellanea probabilistic model for two dependent circular variables.
Motivated by problems in molecular biology and molecular physics, we propose a five-parameter torus analogue of the bivariate normal distribution for modelling the distribution of two circular random variables. The conditional distributions of the proposed distribution are von Mises. The marginal distributions are symmetric around their means and are either unimodal or bimodal. The type of shape d
https://www.cdc.gov/niosh/nioshtic-2/20022894.html
talks.cam : Recent advances in the theory and applications of VAEstalks.cam : Recent advances in the theory and applications of VAEs
If you have a question about this talk, please contact jg801.. Since their development by Kingma et.al.[1] the VAE has proven to be a flexible and powerful framework for latent variable modelling. Sitting at the crossroads of classical statistical learning and deep learning, it has made an impact in many problems, from classical tasks in computer vision such as image generation, compression, super-resolution, inpainting, 3D object synthesis to other tasks such as semi-supervised learning, natural language modelling, sentence interpolation, and even practical applications to medical imaging.. We will start with a minimal introduction to latent variable models and the structure and definition of a VAE , before presenting some examples. We will then discuss some of the topics of interest in the research surrounding VAEs, looking to various techniques meant to reduce the inference gap, as well as studying the usefulness of the VAE for learning latent representations for downstream tasks. If time ...
https://talks.cam.ac.uk/talk/index/149596
Accuracy of clinical neuropsychological versus statistical prediction in the classification of seizure types - University of...Accuracy of clinical neuropsychological versus statistical prediction in the classification of seizure types - University of...
Many studies have reported on the pattern of neuropsychological test performance across varied seizure diagnosis populations. Far fewer studies have evaluated the accuracy of the clinical neuropsychologist in formulating an impression of the seizure diagnosis based on results of neuropsychological assessment, or compared the accuracy of clinical neuropsychological judgment to results of statistical prediction. Accuracy of clinical neuropsychological versus statistical prediction was investigated in four seizure classification scenarios. While both methods outperformed chance, accuracy of clinical neuropsychological classification was either equivalent or superior to statistical prediction. Results support the utility and validity of clinical neuropsychological judgment in epilepsy treatment settings
https://www.upress.umn.edu/test-division/bibliography/2000-2009/2008/fargo_accuracy_2008
Limiting conditional distributions for transient Markov chains on the nonnegative integers conditioned on recurrence to zero<...Limiting conditional distributions for transient Markov chains on the nonnegative integers conditioned on recurrence to zero<...
TY - BOOK. T1 - Limiting conditional distributions for transient Markov chains on the nonnegative integers conditioned on recurrence to zero. AU - Coolen-Schrijner, Pauline. PY - 1994. Y1 - 1994. KW - METIS-142900. M3 - Report. T3 - Memorandum Faculty of Mathematical Sciences. BT - Limiting conditional distributions for transient Markov chains on the nonnegative integers conditioned on recurrence to zero. PB - University of Twente, Faculty of Mathematical Sciences. ER - ...
https://research.utwente.nl/en/publications/limiting-conditional-distributions-for-transient-markov-chains-on
Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and...Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and...
This article reviews the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models, given their growing use in theory testing and construction. Theoretical statistical results i …
https://pubmed.ncbi.nlm.nih.gov/22309957/?dopt=Abstract
Phytopathology 1993 | Using the Beta-Binomial Distribution to Describe Aggregated Patterns of Disease IncidencePhytopathology 1993 | Using the Beta-Binomial Distribution to Describe Aggregated Patterns of Disease Incidence
We discuss the use of the beta-binomial distribution for the description of plant disease incidence data, collected on the basis of scoring plants as either diseased or healthy . The beta-binomial is a discrete probability distribution derived by regarding the probability of a plant being diseased (a constant in the binomial distribution) as a beta-distributed variable. An important characteristic of the beta-binomial is that its variance is larger than that of the binomial distribution with the same mean. The beta-binomial distribution, therefore, may serve to describe aggregated disease incidence data. Using maximum likelihood, we estimated beta-binomial parameters p (mean disease incidence) and ϑ (an index of aggregation) for four previously published sets of disease incidence data in which there were some indications of aggregation. Goodness-of-fit tests showed that, in all these cases, the beta-binomial provided a good description of the observed data and resulted in a better fit than did ...
http://www.apsnet.org/publications/phytopathology/backissues/Documents/1993Abstracts/Phyto_83_759.htm
Zero-Inflated Negative Binomial Regression | R Data Analysis ExamplesZero-Inflated Negative Binomial Regression | R Data Analysis Examples
A zero-inflated model assumes that zero outcome is due to two different processes. For instance, in the example of fishing presented here, the two processes are that a subject has gone fishing vs. not gone fishing. If not gone fishing, the only outcome possible is zero. If gone fishing, it is then a count process. The two parts of the a zero-inflated model are a binary model, usually a logit model to model which of the two processes the zero outcome is associated with and a count model, in this case, a negative binomial model, to model the count process. The expected count is expressed as a combination of the two processes. Taking the example of fishing again:. $$ E(n_{\text{fish caught}} = k) = P(\text{not gone fishing}) * 0 + P(\text{gone fishing}) * E(y = k , \text{gone fishing}) $$. To understand the zero-inflated negative binomial regression, lets start with the negative binomial model. There are multiple parameterizations of the negative binomial model, we focus on NB2. The negative ...
https://stats.idre.ucla.edu/r/dae/zinb/
On estimation of partially linear transformation models<...On estimation of partially linear transformation models<...
TY - JOUR. T1 - On estimation of partially linear transformation models. AU - Lu, Wenbin. AU - Zhang, Hao Helen. PY - 2010/6. Y1 - 2010/6. N2 - We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernelweighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unified manner. We show that with a proper choice of the kernel bandwidth parameter, one can obtain the consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established as well.We further suggest a simple resampling method to estimate the asymptotic variance of the linear estimates and show its effectiveness. To facilitate the implementation of the new procedure, an iterative ...
https://arizona.pure.elsevier.com/en/publications/on-estimation-of-partially-linear-transformation-models
Sample Size Formulas for Estimating Intraclass Correlation Coefficient by Mengxiao XuSample Size Formulas for Estimating Intraclass Correlation Coefficient by Mengxiao Xu
Introduction: Measurement errors can seriously affect quality of clinical practice and medical research. It is therefore important to assess such errors by conduct- ing studies to estimate a coefficients reliability and assessing its precision. The intraclass correlation coefficient (ICC), defined on a model that an observation is a sum of information and random error, has been widely used to quantify reliability for continuous measurements. Sample formulas have been derived for explicitly incorporation of a prespecified probability of achieving the prespecified precision, i.e., the width or lower limit of a confidence interval for ICC. Although the concept of ICC is applicable to binary outcomes, existed sample size formulas for this case can only provide about 50% assurance probability to achieve the desired precision. Methods: A common correlation model was adopted to characterize binary data arising from reliability studies. A large sample variance estimator for ICC was derived, which was then used
https://ir.lib.uwo.ca/etd/4099/
Marginal structural model - WikipediaMarginal structural model - Wikipedia
Marginal structural models are a class of statistical models used for causal inference in epidemiology. Such models handle the issue of time-dependent confounding in evaluation of the efficacy of interventions by inverse probability weighting for receipt of treatment. For instance, in the study of the effect of zidovudine in AIDS-related mortality, CD4 lymphocyte is used both for treatment indication, is influenced by treatment, and affects survival. Time-dependent confounders are typically highly prognostic of health outcomes and applied in dosing or indication for certain therapies, such as body weight or lab values such as alanine aminotransferase or bilirubin. Robins, James; Hernán, Miguel; Brumback, Babette (September 2000). Marginal Structural Models and Causal Inference in Epidemiology (PDF). Epidemiology. 11 (5): 550-60. doi:10.1097/00001648-200009000-00011. PMID 10955408. https://epiresearch.org/ser50/serplaylists/introduction-to-marginal-structural-models ...
https://en.wikipedia.org/wiki/Marginal_structural_model
Categorical Data Analysis, 3rd Edition | Categorical Data Analysis | Data Analysis | General & Introductory Statistics |...Categorical Data Analysis, 3rd Edition | Categorical Data Analysis | Data Analysis | General & Introductory Statistics |...
Preface xiii. 1 Introduction: Distributions and Inference for Categorical Data 1. 1.1 Categorical Response Data, 1. 1.2 Distributions for Categorical Data, 5. 1.3 Statistical Inference for Categorical Data, 8. 1.4 Statistical Inference for Binomial Parameters, 13. 1.5 Statistical Inference for Multinomial Parameters, 17. 1.6 Bayesian Inference for Binomial and Multinomial Parameters, 22. Notes, 27. Exercises, 28. 2 Describing Contingency Tables 37. 2.1 Probability Structure for Contingency Tables, 37. 2.2 Comparing Two Proportions, 43. 2.3 Conditional Association in Stratified 2 × 2 Tables, 47. 2.4 Measuring Association in I × J Tables, 54. Notes, 60. Exercises, 60. 3 Inference for Two-Way Contingency Tables 69. 3.1 Confidence Intervals for Association Parameters, 69. 3.2 Testing Independence in Two-way Contingency Tables, 75. 3.3 Following-up Chi-Squared Tests, 80. 3.4 Two-Way Tables with Ordered Classifications, 86. 3.5 Small-Sample Inference for Contingency Tables, 90. 3.6 Bayesian ...
https://www.wiley.com/en-us/Categorical+Data+Analysis%2C+3rd+Edition-p-9781118710944
Empirical Analysis of the performance of variance estimators in sequential single-run ranking & selection: The case of time...Empirical Analysis of the performance of variance estimators in sequential single-run ranking & selection: The case of time...
TY - GEN. T1 - Empirical Analysis of the performance of variance estimators in sequential single-run ranking & selection. T2 - 2016 Winter Simulation Conference, WSC 2016. AU - Pedrielli, Giulia. AU - Zhu, Yinchao. AU - Lee, Loo Hay. AU - Li, Haobin. PY - 2017/1/17. Y1 - 2017/1/17. N2 - Ranking and Selection has acquired an important role in the Simulation-Optimization field, where the different alternatives can be evaluated by discrete event simulation (DES). Black box approaches have dominated the literature by interpreting the DES as an oracle providing i.i.d. observations. Another relevant family of algorithms, instead, runs each simulator once and observes time series. This paper focuses on such a method, Time Dilation with Optimal Computing Budget Allocation (TD-OCBA), recently developed by the authors. One critical aspect of TD-OCBA is estimating the response given correlated observations. In this paper, we are specifically concerned with the estimator of the variance of the response ...
https://asu.pure.elsevier.com/en/publications/empirical-analysis-of-the-performance-of-variance-estimators-in-s
Power analysis of the likelihood ratio test for logistic regression mixturesPower analysis of the likelihood ratio test for logistic regression mixtures
Finite mixture models emerge in many applications, particularly in biology, psychology and genetics. This dissertation focused on detecting associations between a quantitative explanatory variable and a dichotomous response variable in a situation where the population consists of a mixture. That is, there is a fraction of the population for whom there is an association between the quantitative predictor and the response and there is a fraction of individuals for whom there is no association between the quantitative predictor and the response. We developed the Likelihood Ratio Test (LRT) in the context of ordinary logistic regression models and logistic regression mixture models. However, the classical theorem for the null distribution of the LRT statistics can not be applied to finite mixture alternatives. Thus, we conjectured that the asymptotic null distribution of the LRT statistics held. We investigated how the empirical and fitted null distribution of the LRT statistics compared with our ...
https://dspace.sunyconnect.suny.edu/handle/1951/56048
A New Ridge Estimator in Linear Measurement Error Model with Stochastic Linear Restrictions - Journal of The Iranian...A New Ridge Estimator in Linear Measurement Error Model with Stochastic Linear Restrictions - Journal of The Iranian...
In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this ...
http://jirss.irstat.ir/article-1-360-en.html
1004.2316] Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning...1004.2316] Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning...
Abstract: In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many learning machines are singular statistical models, the asymptotic behavior of the cross-validation remains unknown. In previous studies, we established the singular learning theory and proposed a widely applicable information criterion, the expectation value of which is asymptotically equal to the average Bayes generalization loss. In the present paper, we theoretically compare the Bayes cross-validation loss and the widely applicable information criterion and prove two theorems. First, the Bayes cross-validation loss is asymptotically equivalent to the widely applicable information criterion as a random variable. Therefore, model selection and hyperparameter optimization using these two values are asymptotically equivalent. Second, the sum of the Bayes generalization error and the Bayes cross-validation error is asymptotically equal to ...
https://arxiv.org/abs/1004.2316
Bivariate categorical data analysis using normal linear conditional multinomial probability model. - Semantic ScholarBivariate categorical data analysis using normal linear conditional multinomial probability model. - Semantic Scholar
Bivariate multinomial data such as the left and right eyes retinopathy status data are analyzed either by using a joint bivariate probability model or by exploiting certain odds ratio-based association models. However, the joint bivariate probability model yields marginal probabilities, which are complicated functions of marginal and association parameters for both variables, and the odds ratio-based association model treats the odds ratios involved in the joint probabilities as working parameters, which are consequently estimated through certain arbitrary working regression models. Also, this later odds ratio-based model does not provide any easy interpretations of the correlations between two categorical variables. On the basis of pre-specified marginal probabilities, in this paper, we develop a bivariate normal type linear conditional multinomial probability model to understand the correlations between two categorical variables. The parameters involved in the model are consistently estimated
https://www.semanticscholar.org/paper/Bivariate-categorical-data-analysis-using-normal-l-Sun-Sutradhar/cc6650ba9d98a485267530e67b751b9aaaf3581f
Yongli Shuai: Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored... > Weekly Update | Pitt Public...Yongli Shuai: Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored... > Weekly Update | Pitt Public...
Yongli Shuai of the Department of Biostatistics defends his dissertation on Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored Competing Risks Data. Graduate faculty of the University and all other interested parties are invit...
https://www.publichealth.pitt.edu/weeklyupdate/ctl/details/mid/11131/itemid/2862
Clustering patient length of stay using mixtures of Gaussian models and phase type distributions<...Clustering patient length of stay using mixtures of Gaussian models and phase type distributions<...
TY - GEN. T1 - Clustering patient length of stay using mixtures of Gaussian models and phase type distributions. AU - Garg, Lalit. AU - McClean, Sally. AU - Meenan, BJ. AU - El-Darzi, Elia. AU - Millard, Peter. PY - 2009. Y1 - 2009. N2 - Gaussian mixture distributions and Coxian phase type distributions have been popular choices model based clustering of patients length of stay data. This paper compares these models and presents an idea for a mixture distribution comprising of components of both of the above distributions. Also a mixed distribution survival tree is presented. A stroke dataset available from the English Hospital Episode Statistics database is used as a running example.. AB - Gaussian mixture distributions and Coxian phase type distributions have been popular choices model based clustering of patients length of stay data. This paper compares these models and presents an idea for a mixture distribution comprising of components of both of the above distributions. Also a mixed ...
https://ulster-staging.pure.elsevier.com/en/publications/clustering-patient-length-of-stay-using-mixtures-of-gaussian-mode-3
Time-series count data regressionTime-series count data regression
The count data model studied in the paper extends the Poisson model by allowing for overdispersion and serial correlation. Alternative approaches to estimate nuisance parameters, required for the correction of the Poisson maximum likelihood covariance matrix estimator and for a quasi-likelihood estimator, are studied. The estimators are evaluated by finite sample Monte Carlo experimentation. It is found that the Poisson maximum likelihood estimator with corrected covariance matrix estimators provide reliable inferences for longer time series. Overdispersion test statistics are wellbehaved, while conventional portmanteau statistics for white noise have too large sizes. Two empirical illustrations are included.. ...
http://umu.diva-portal.org/smash/record.jsf?pid=diva2:603196
Remote Sensing | Free Full-Text | Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution | HTMLRemote Sensing | Free Full-Text | Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution | HTML
Changes in the spatial distributions of vegetation across the globe are routinely monitored by satellite remote sensing, in which the reflectance spectra over land surface areas are measured with spatial and temporal resolutions that depend on the satellite instrumentation. The use of multiple synchronized satellite sensors permits long-term monitoring with high spatial and temporal resolutions. However, differences in the spatial resolution of images collected by different sensors can introduce systematic biases, called scaling effects, into the biophysical retrievals. This study investigates the mechanism by which the scaling effects distort normalized difference vegetation index (NDVI). This study focused on the monotonicity of the area-averaged NDVI as a function of the spatial resolution. A monotonic relationship was proved analytically by using the resolution transform model proposed in this study in combination with a two-endmember linear mixture model. The monotonicity allowed the inherent
http://www.mdpi.com/2072-4292/4/1/160/htm
Risk prediction models for dementia: role of age and cardiometabolic risk factors | BMC Medicine | Full TextRisk prediction models for dementia: role of age and cardiometabolic risk factors | BMC Medicine | Full Text
Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) risk score is the only currently available midlife risk score for dementia. We compared CAIDE to Framingham cardiovascular Risk Score (FRS) and FINDRISC diabetes score as predictors of dementia and assessed the role of age in their associations with dementia. We then examined whether these risk scores were associated with dementia in those free of cardiometabolic disease over the follow-up. A total of 7553 participants, 39-63 years in 1991-1993, were followed for cardiometabolic disease (diabetes, coronary heart disease, stroke) and dementia (N = 318) for a mean 23.5 years. Cox regression was used to model associations of age at baseline, CAIDE, FRS, and FINDRISC risk scores with incident dementia. Predictive performance was assessed using Roystons R2, Harrells C-index, Akaikes information criterion (AIC), the Greenwood-Nam-DAgostino (GND) test, and calibration-in-the-large. Age effect was also assessed by stratifying analyses by
https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01578-x
Fields Institute - Nonparametric Statistics ConferenceFields Institute - Nonparametric Statistics Conference
Following the tradition of Carleton University yet another International Conference on Nonparametric methods for Measurement Error Models and Related topics has been arranged. In recent years, the scope of application of measurement error models has widened in biostatistics, bio -pharmakinetics , and DNA analysis to name a few among others. Our aim is to rejuvenate the research activity in nonparametric statistics by bringing scholars from all around the globe in this conference to exchange ideas for future developments to meet the need of application . Although, there is vast amount of literature on this topic, there is slow growth in developing robust inference techniques such as rank test and estimation , shrinkage estimation and S-test and estimation in measurement error models. This conference will focus on the current activities in parametric and nonparametric methods and consider new directions on the approach one should take developing these area of research ...
http://www.fields.utoronto.ca/programs/scientific/08-09/nonparametric/
Download Advances In Growth Curve Models: Topics From The Indian Statistical Institute 2013Download Advances In Growth Curve Models: Topics From The Indian Statistical Institute 2013
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http://canadabiketours.de/images/lib/download-Advances-in-Growth-Curve-Models%3A-Topics-from-the-Indian-Statistical-Institute-2013.php
Efficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood - Munich Personal RePEc ArchiveEfficient Estimation of Approximate Factor Models via Regularized Maximum Likelihood - Munich Personal RePEc Archive
Alessi, L., Barigozzi, M. and Capassoc, M. (2010). Improved penalization for determining the number of factors in approximate factor models. Statistics and Probability Letters, 80, 1806-1813.. Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica. 71 135-171.. Bai, J. and Li, K. (2012). Statistical analysis of factor models of high dimension. Ann. Statist. 40, 436-465.. Bai, J. and Ng, S.(2002). Determining the number of factors in approximate factor models. Econometrica. 70 191-221.. Bickel, P. and Levina, E. (2008a). Covariance regularization by thresholding. Ann. Statist. 36 2577-2604.. Bickel, P. and Levina, E. (2008b). Regularized estimation of large covariance matrices. Ann. Statist. 36 199-227.. Bien, J. and Tibshirani, R. (2011). Sparse estimation of a covariance matrix. Biometrika. 98, 807-820.. Breitung, J. and Tenhofen, J. (2011). GLS estimation of dynamic factor models. J. Amer. Statist. Assoc. 106, 1150-1166.. Cai, T. and Liu, W. (2011). Adaptive ...
https://mpra.ub.uni-muenchen.de/41641/
Statistical Inference Using Maximum Likelihood Estimation and the Generalized Likelihood Ratio when the True Parameter is on...Statistical Inference Using Maximum Likelihood Estimation and the Generalized Likelihood Ratio when the True Parameter is on...
Statistical Inference Using Maximum Likelihood Estimation and the Generalized Likelihood Ratio when the True Parameter is on the Boundary of the Parameter Space* (Feng, Ziding; McCulloch, Charles E.) 13 ...
https://ecommons.cornell.edu/handle/1813/31683
Statistical analysis of a class of factor time series models<...Statistical analysis of a class of factor time series models<...
TY - JOUR. T1 - Statistical analysis of a class of factor time series models. AU - Taniguchi, Masanobu. AU - Maeda, Kousuke. AU - Puri, Madan L.. PY - 2006/7/1. Y1 - 2006/7/1. N2 - For a class of factor time series models, which is called a multivariate time series variance component (MTV) models, we consider the problem of testing whether an observed time series belongs to this class. We propose the test statistic, and derive its symptotic null distribution. Asymptotic optimality of the proposed test is discussed in view of the local asymptotic normality. Also, numerical evaluation of the local power illuminates some interesting features of the test.. AB - For a class of factor time series models, which is called a multivariate time series variance component (MTV) models, we consider the problem of testing whether an observed time series belongs to this class. We propose the test statistic, and derive its symptotic null distribution. Asymptotic optimality of the proposed test is discussed in ...
https://waseda.pure.elsevier.com/en/publications/statistical-analysis-of-a-class-of-factor-time-series-models
Lundi 15 décembre 2015 | MaIAGELundi 15 décembre 2015 | MaIAGE
Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. We introduce a new theoretical approach based on the convergence of an iterated Bayes map. A new algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process.. ...
https://maiage.inrae.fr/fr/node/578
A statistical method for genotypic association that is robust to sequencing misclassificationA statistical method for genotypic association that is robust to sequencing misclassification
DescriptionIn analyzing human genetic disorders, association analysis is one of the most commonly used approaches. However, there are challenges with association analysis, including differential misclassification in data that inflates the false-positive rate. In this thesis, I present a new statistical method for testing the association between disease phenotypes and multiple single nucleotide polymorphisms (SNPs). This method uses next-generation sequencing (NGS) raw data and is robust to sequencing differential misclassification. By incorporating expectation-maximization (EM) algorithm, this method computes the test statistic and estimates important parameters of the model, including misclassification. By performing simulation studies, I report that this method maintains correct type I error rates and may obtain high statistical power. ...
https://rucore.libraries.rutgers.edu/rutgers-lib/54057/
Download Quantum Statistical Theory Of SuperconductivityDownload Quantum Statistical Theory Of Superconductivity
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http://ufovideo.net/ebook/download-quantum-statistical-theory-of-superconductivity
Implement Bayesian Variable SelectionImplement Bayesian Variable Selection
function [logPrior,gradient] = logPDFBVS(params,mu,vin,vout,pGamma,a,b) %logPDFBVS Log joint prior for Bayesian variable selection % logPDFBVS is the log of the joint prior density of a % normal-inverse-gamma mixture conjugate model for a Bayesian linear % regression model with numCoeffs coefficients. logPDFBVS passes % params(1:end-1), the coefficients, to the PDF of a mixture of normal % distributions with hyperparameters mu, vin, vout, and pGamma, and also % passes params(end), the disturbance variance, to an inverse gamma % density with shape a and scale b. % % params: Parameter values at which the densities are evaluated, a % (numCoeffs + 1)-by-1 numeric vector. The first numCoeffs % elements correspond to the regression coefficients and the last % element corresponds to the disturbance variance. % % mu: Multivariate normal component means, a numCoeffs-by-1 numeric % vector of prior means for the regression coefficients. % % vin: Multivariate normal component scales, a numCoeffs-by-1 vector ...
http://www.mathworks.com/examples/econometrics/mw/econ-ex93053255-implement-bayesian-variable-selection
Improved method for correction of systematic bias introduced by the sub-voxel image registration process in functional magnetic...Improved method for correction of systematic bias introduced by the sub-voxel image registration process in functional magnetic...
During functional magnetic resonance imaging (fMRI) brain examinations, the signal extraction from a large number of images is used to evaluate changes in blood oxygenation levels by applying statistical methodology. Image registration is essential as it assists in providing accurate fractional positioning accomplished by using interpolation between sequentially acquired fMRI images. Unfortunately, current subvoxel registration methods found in standard software may produce significant bias in the variance estimator when interpolating with fractional, spatial voxel shifts. It was found that interpolation schemes, as currently applied during the registration of functional brain images, could introduce statistical bias, but there is a possible correction scheme. This bias was shown to result from the weighted-averaging process employed by conventional implementation of interpolation schemes. The most severe consequence of inaccurate variance estimators is the undesirable violation of the ...
https://www.spie.org/Publications/Proceedings/Paper/10.1117/12.655309
Conditional transformation models for survivor function estimation - Zurich Open Repository and ArchiveConditional transformation models for survivor function estimation - Zurich Open Repository and Archive
In survival analysis, the estimation of patient-specific survivor functions that are conditional on a set of patient characteristics is of special interest. In general, knowledge of the conditional survival probabilities of a patient at all relevant time points allows better assessment of the patients risk than summary statistics, such as median survival time. Nevertheless, standard methods for analysing survival data seldom estimate the survivor function directly. Therefore, we propose the application of conditional transformation models (CTMs) for the estimation of the conditional distribution function of survival times given a set of patient characteristics. We used the inverse probability of censoring weighting approach to account for right-censored observations. Our proposed modelling approach allows the prediction of patient-specific survivor functions. In addition, CTMs constitute a flexible model class that is able to deal with proportional as well as non-proportional hazards. The ...
https://www.zora.uzh.ch/id/eprint/117624/
Statistical Analysis in Empirical Bayes and in Causal inference by Hui NieStatistical Analysis in Empirical Bayes and in Causal inference by Hui Nie
In Part I titled Empirical Bayes Estimation, we discuss the estimation of a heteroscedastic multivariate normal mean in terms of the ensemble risk. We first derive the ensemble minimax properties of various estimators that shrink towards zero through the empirical Bayes method. We then generalize our results to the case where the variances are given as a common unknown but estimable chi-squared random variable scaled by different known factors. We further provide a class of ensemble minimax estimators that shrink towards the common mean. We also make comparison and show differences between results from the heteroscedastic case and those from the homoscedastic model.In Part II titled Causal Inference Analysis, we study the estimation of the causal effect of treatment on survival probability up to a given time point among those subjects who would comply with the assignment to both treatment and control when both administrative censoring and noncompliance occur. In many clinical studies with a survival
https://repository.upenn.edu/edissertations/339/
British Library EThOS: Smooth risk functions for self-controlled case series modelsBritish Library EThOS: Smooth risk functions for self-controlled case series models
The self-controlled case series (SeeS) method is commonly used to investigate associations between vaccine exposures and adverse events (side effects). It is an alternative to cohort and case control study designs. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounders that could modify the true association between exposure and adverse event. However, timevarying confounders (age, season) are not automatically controlled. The sees method has parametric and semi-parametric versions in terms of controlling the age effect. The parametric method uses piecewise constant functions with a priori chosen age ~ . groups and the semi-parametric method leaves the age effect unspecified. Mis-specification of age groups in the parametric version may lead to biased estimates of the exposure effect, and the semi-parametric approach runs into computational problems when the sample size is moderately large . ...
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607161
TENSOR REGRESSION AND TENSOR TIME SERIES ANALYSES FOR HIGH DIMENSIONAL by Herath Mudiyanselage Wiranthe Bandara HerathTENSOR REGRESSION AND TENSOR TIME SERIES ANALYSES FOR HIGH DIMENSIONAL by Herath Mudiyanselage Wiranthe Bandara Herath
Many real data are naturally represented as a multidimensional array called a tensor. In classical regression and time series models, the predictors and covariate variables are considered as a vector. However, due to high dimensionality of predictor variables, these types of models are inefficient for analyzing multidimensional data. In contrast, tensor structured models use predictors and covariate variables in a tensor format. Tensor regression and tensor time series models can reduce high dimensional data to a low dimensional framework and lead to efficient estimation and prediction. In this thesis, we discuss the modeling and estimation procedures for both tensor regression models and tensor time series models. The results of simulation studies and a numerical analysis are provided.
https://opensiuc.lib.siu.edu/theses/2585/
Testing for A Change Point by Model Selection Tools in Linear Regression Models -Communications for Statistical...Testing for A Change Point by Model Selection Tools in Linear Regression Models -Communications for Statistical...
Several information criterions, Schwarz information criterion (SIC), Akaike information criterion (AIC), and the modified Akaike information criterion (|TEX|$AIC_c$|/TEX|), are proposed to locate a change point in the multiple linear regression model. These methods are applied to a stock Exchange data set and compared to the results.
http://www.koreascience.or.kr/article/JAKO200011920777831.page
Sensitivity of genomic selection to using different prior distributions | BMC Proceedings | Full TextSensitivity of genomic selection to using different prior distributions | BMC Proceedings | Full Text
The other two models assumed mixture distributions for the SNP effects reflecting the assumption that there is a large number of SNPs with zero or near zero effects and a second smaller set of SNPs with larger significant effects. A Bayes A/B hybrid method was used. This approximation to Bayes B [1] was used to keep computational and time demands reasonable. In this algorithm, after every k Bayes A iterations, Bayes B via the reverse jump algorithm is employed. The Reverse Jump algorithm [3] is run multiple times per SNP and then any SNP with a final state of zero in the current Bayes B iterations is set to zero for the subsequent k iterations of the Bayes A. This maintains the correct transitions between models of differing dimensionality. The prior distributions are identical to that of the original Bayes B using a mixture prior distribution for the SNP variance allowing a proportion, 1-π, to be set to zero. The other proportion π is sampled from the same mixture distribution as Bayes A. ...
https://bmcproc.biomedcentral.com/articles/10.1186/1753-6561-4-S1-S5
Aspects of Multivariate Statistical Theory - Robb J. Muirhead - Google BooksAspects of Multivariate Statistical Theory - Robb J. Muirhead - Google Books
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. . . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to penetrate deeper into the multivariate field. -Mededelingen van het Wiskundig Genootschap This book is a comprehensive and clearly written text on multivariate analysis from a theoretical point of view. -The Statistician Aspects of Multivariate Statistical Theory presents a classical mathematical treatment of the techniques, distributions, and inferences based on multivariate normal distribution. Noncentral
https://books.google.com/books?id=5xrvAAAAMAAJ&q=elliptical+distribution&dq=related:ISBN0821802496&lr=&source=gbs_word_cloud_r&hl=en
Statistical models for genetic susceptibility in toxicological and epidemiological investigations<...Statistical models for genetic susceptibility in toxicological and epidemiological investigations<...
TY - JOUR. T1 - Statistical models for genetic susceptibility in toxicological and epidemiological investigations. AU - Piegorsch, W. W.. PY - 1994/1/1. Y1 - 1994/1/1. N2 - Models are presented for use in assessing genetic susceptibility to cancer (or other diseases) with animal or human data. Observations are assumed to be in the form of proportions, hence a binomial sampling distribution is considered. Generalized linear models are employed to model the response as a function of the genetic component; these include logistic and complementary log forms. Susceptibility is measured via odds ratios of response. relative to a background genetic group. Significance tests and confidence intervals for these odds ratios are based on maximum likelihood estimates of the regression parameters. Additional consideration is given to the problem of gene-environment interactions and to testing whether certain genetic identifiers/categories may be collapsed into a smaller set of categories. The collapsibility ...
https://arizona.pure.elsevier.com/en/publications/statistical-models-for-genetic-susceptibility-in-toxicological-an
Joint and Marginal Probability Density Functions of IndependentJoint and Marginal Probability Density Functions of Independent
Let X, Y be independent, standard normal random variables, and let U = X + Y and V = X - Y. (a) Find the joint probability density function of (U, V) and specify its domain. (b) Find the marginal probability density function of U.
https://brainmass.com/statistics/probability-density-function/joint-marginal-probability-density-functions-independent-variables-45665
On the efficiency of bootstrap method into the analysis contingency tableOn the efficiency of bootstrap method into the analysis contingency table
The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorica ldata analysis,inparticular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.. ...
http://uu.diva-portal.org/smash/record.jsf?pid=diva2:441926
Matrix-exponential distributionMatrix-exponential distribution
Bean, N. G.; Fackrell, M.; Taylor, P. (2008). "Characterization of Matrix-Exponential Distributions". Stochastic Models. 24 (3 ... Encyclopedia of Statistical Sciences. doi:10.1002/0471667196.ess1092.pub2. ISBN 0471667196. ... Fackrell, M. (2005). "Fitting with Matrix-Exponential Distributions". Stochastic Models. 21 (2-3): 377. doi:10.1081/STM- ...
https://en.wikipedia.org/wiki/Matrix-exponential_distribution
Markov random fieldMarkov random field
This model is equivalent to the clique factorization model given above, if N k = , dom ⁡ ( C k ) , {\displaystyle N_{k}=,\ ... The importance of the partition function Z is that many concepts from statistical mechanics, such as entropy, directly ... Constraint composite graph Graphical model Dependency network (graphical model) Hammersley-Clifford theorem Hopfield network ... evaluating the likelihood or gradient of the likelihood of a model requires inference in the model, which is generally ...
https://en.wikipedia.org/wiki/Markov_random_field
Brian CaffoBrian Caffo
Advanced Linear Models for Data Science 2: Statistical Linear Models; Statistical Inference; Regression Models; Developing Data ... Advanced Linear Models for Data Science 1: Least Squares; ...
https://en.wikipedia.org/wiki/Brian_Caffo
Pierre-Louis LionsPierre-Louis Lions
1. Incompressible models. Oxford Lecture Series in Mathematics and its Applications, 3. Oxford Science Publications. The ... On Euler equations and statistical physics. Cattedra Galileiana. [Galileo Chair] Scuola Normale Superiore, Classe di Scienze, ... 2. Compressible models. Oxford Lecture Series in Mathematics and its Applications, 10. Oxford Science Publications. The ... The mathematical theory of thermodynamic limits: Thomas-Fermi type models. Oxford Mathematical Monographs. The Clarendon Press ...
https://en.wikipedia.org/wiki/Pierre-Louis_Lions
Functional regressionFunctional regression
Fan and Zhang (1999). "Statistical estimation in varying coefficient models". The Annals of Statistics. 27 (5):1491-1518. doi: ... Functional linear models (FLMs) are an extension of linear models (LMs). A linear model with scalar response Y ∈ R {\ ... In particular, functional polynomial models, functional single and multiple index models and functional additive models are ... in model (4). A simpler version of the historical functional linear model is the functional concurrent model (see below). ...
https://en.wikipedia.org/wiki/Functional_regression
Deviance (statistics)Deviance (statistics)
In statistics, deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis ... The deviance is used to compare two models - in particular in the case of generalized linear models (GLM) where it has a ... Then, under the null hypothesis that M2 is the true model, the difference between the deviances for the two models follows, ... It plays an important role in exponential dispersion models and generalized linear models. The unit deviance d ( y , μ ) {\ ...
https://en.wikipedia.org/wiki/Deviance_(statistics)
Least squaresLeast squares
The researcher specifies an empirical model in regression analysis. A very common model is the straight-line model, which is ... Similarly, statistical tests on the residuals can be conducted if the probability distribution of the residuals is known or ... A regression model is a linear one when the model comprises a linear combination of the parameters, i.e., f ( x , β ) = ∑ j = 1 ... The goal is to find the parameter values for the model that "best" fits the data. The fit of a model to a data point is ...
https://en.wikipedia.org/wiki/Least_squares
John NelderJohn Nelder
In statistical theory, Nelder and Wedderburn proposed the generalized linear model. Generalized linear models were formulated ... about statistical modelling, includes some reminiscences about John. JN and R. W. M. Wedderburn, "Generalized Linear Models", J ... Nelder, John; Wedderburn, Robert (1972). "Generalized Linear Models". Journal of the Royal Statistical Society. Series A ( ... "for their monograph Generalized Linear Models (1983)". As tribute on his eightieth birthday, a festschrift Methods and Models ...
https://en.wikipedia.org/wiki/John_Nelder
Harvey GoldsteinHarvey Goldstein
He was author of a monograph on multilevel statistical models. He came from a left-wing family, and as a teenager he briefly ... Goldstein, Harvey (2003). Multilevel Statistical Models. Kendall's Library of Statistics (3rd ed.). London: Arnold. ISBN 0-340- ... He was elected a fellow of the British Academy in 1996 and awarded the Guy Medal in silver by the Royal Statistical Society in ... He was professor of social statistics in the Centre for Multilevel Modelling at the University of Bristol. From 1977 to 2005, ...
https://en.wikipedia.org/wiki/Harvey_Goldstein
Joop HoxJoop Hox
Multilevel statistical models. Vol. 922. John Wiley & Sons, 2011. Joop Hox at University of Utrecht Joop Hox homepage. ... Special modeling techniques have been developed to map this kind of data. These techniques can often significantly improve the ... "Sufficient sample sizes for multilevel modeling." Methodology: European Journal of Research Methods for the Behavioral and ... known for his work in the field of social research method such as survey research and multilevel modeling. Hox attended the ...
https://en.wikipedia.org/wiki/Joop_Hox
Logistic regressionLogistic regression
... is a statistical model that in its basic form uses a logistic function to model a binary dependent variable ... likelihood of null model likelihood of the saturated model − ln ⁡ likelihood of fitted model likelihood of the saturated model ... see Probit model § History. The probit model influenced the subsequent development of the logit model and these models competed ... likelihood of null model likelihood of the saturated model ) ( likelihood of fitted model likelihood of the saturated model ...
https://en.wikipedia.org/wiki/Logistic_regression
Oscar KempthorneOscar Kempthorne
Kempthorne was skeptical of "statistical models" (of populations), when such models are proposed by statisticians rather than ... 1984). Experimental design, statistical models, and genetic statistics: Essays in honor of Oscar Kempthorne. Statistics: ... Bancroft, T. A. (1984). "The years 1950-1972". In Klaus Hinkelmann (ed.). Experimental design, statistical models, and genetic ... David, H. A. (1984). "The years 1972-1984". In Klaus Hinkelmann (ed.). Experimental design, statistical models, and genetic ...
https://en.wikipedia.org/wiki/Oscar_Kempthorne
Andrej PazmanAndrej Pazman
He obtained the Price of the Slovak Literary Fund for the Nonlinear statistical models (1994). In 2004, he obtained the WU Best ... Andrej Pázman (1993). Nonlinear statistical models. Kluwer Acad. Publ., Dordrecht. Pronzato Luc and Andrej Pázman (2013). ... CS1 maint: discouraged parameter (link) "Nonlinear statistical models". Retrieved January 18, 2017. CS1 maint: discouraged ... is a Slovak mathematician working in the area of optimum experimental design and in the theory of nonlinear statistical models ...
https://en.wikipedia.org/wiki/Andrej_Pazman
Deming regressionDeming regression
Most statistical software packages used in clinical chemistry offer Deming regression. The model was originally introduced by ... In statistics, Deming regression, named after W. Edwards Deming, is an errors-in-variables model which tries to find the line ... Deming regression is equivalent to the maximum likelihood estimation of an errors-in-variables model in which the errors for ... Deming, W. E. (1943). Statistical adjustment of data. Wiley, NY (Dover Publications edition, 1985). ISBN 0-486-64685-8. Fuller ...
https://en.wikipedia.org/wiki/Deming_regression
Imprecise probabilityImprecise probability
Interval Statistical Models. Moscow: Radio i Svyaz Publ. Ruggeri, Fabrizio (2000). Robust Bayesian Analysis. D. Ríos Insua. New ... This issue may be merely rhetorical, as the robustness of a model with intervals is inherently greater than that of a model ... Smith, Cedric A. B. (1961). "Consistency in statistical inference and decision". Journal of the Royal Statistical Society. B ( ... Uncertainty is traditionally modelled by a probability distribution, as developed by Kolmogorov, Laplace, de Finetti, Ramsey, ...
https://en.wikipedia.org/wiki/Imprecise_probability
Rosemary A. BaileyRosemary A. Bailey
ISBN 978-0-521-68357-9. Speed, T. P.; Bailey, R. A. (1987). "Factorial Dispersion Models". International Statistical Review / ... She has written books on the design of experiments, on association schemes, and on linear models in statistics. Bailey earned ... International Statistical Institute (ISI). 55 (3): 261-277. doi:10.2307/1403405. JSTOR 1403405. Rosemary A. Bailey at the ... Bailey, R. A. (1994). Normal linear models. London: External Advisory Service, University of London. ISBN 0-7187-1176-9. Bailey ...
https://en.wikipedia.org/wiki/Rosemary_A._Bailey
ML.NETML.NET
Hastie, Trevor J. (1 November 2017). "Generalized Additive Models". Statistical Models in S. pp. 249-307. doi:10.1201/ ... Developers can train a Machine Learning Model or reuse an existing Model by a 3rd party and run it on any environment offline. ... The ML.NET Model Builder preview is an extension for Visual Studio that uses ML.NET CLI and ML.NET AutoML to output the best ML ... The ML.NET CLI is a Command-line interface which uses ML.NET AutoML to perform model training and pick the best algorithm for ...
https://en.wikipedia.org/wiki/ML.NET
William S. ClevelandWilliam S. Cleveland
"Local regression models." Statistical models in S (1992): 309-376. Armitage, Peter, Geoffrey Berry, and John NS Matthews. ... statistical modeling, visual perception, environmental science, and seasonal adjustment." Cleveland is credited with defining ... Statistical methods in medical research. John Wiley & Sons, 2008. Venables, William N., and Brian D. Ripley. Modern applied ... In 1982 he was elected as a Fellow of the American Statistical Association. His research interests are in the fields of "data ...
https://en.wikipedia.org/wiki/William_S._Cleveland
Principle of marginalityPrinciple of marginality
Nelder, J. A. (1977). "A Reformulation of Linear Models". Journal of the Royal Statistical Society. 140 (1): 48-77. doi:10.2307 ... to model interaction effects but delete main effects that are marginal to them. While such models are interpretable, they lack ... With this model, the effect of x upon y is given by the partial derivative of y with respect to x; this is b + d z i {\ ... The above regression model, with two independent continuous variables, is presented with a numerical example, in Stata, as Case ...
https://en.wikipedia.org/wiki/Principle_of_marginality
Repeated measures designRepeated measures design
ISBN 0-8247-9341-2. Pan, Jian-Xin & Fang, Kai-Tai (2002). Growth curve models and statistical diagnostics. Springer Series in ... and the multivariate test statistic is reported. A third effect size statistic that is reported is the generalized η2, which is ... The F statistic is the same as in the Standard Univariate ANOVA F test, but is associated with a more accurate p-value. This ... As with all statistical analyses, specific assumptions should be met to justify the use of this test. Violations can moderately ...
https://en.wikipedia.org/wiki/Repeated_measures_design
LogSumExpLogSumExp
Statistical Rethinking. OCLC 1107423386. "Practical issues: Numeric stability". CS231n Convolutional Neural Networks for Visual ... El Ghaoui, Laurent (2017). Optimization Models and Applications. "convex analysis - About the strictly convexity of log-sum-exp ...
https://en.wikipedia.org/wiki/LogSumExp
Welch-Satterthwaite equationWelch-Satterthwaite equation
Applied Linear Statistical Models. Richard D. Irwin, Inc. ISBN 0-256-08338-X. Michael Allwood (2008) "The Satterthwaite Formula ... The result can be used to perform approximate statistical inference tests. The simplest application of this equation is in ...
https://en.wikipedia.org/wiki/Welch–Satterthwaite_equation
Working-Hotelling procedureWorking-Hotelling procedure
Neter, John; Wasserman, William; Kutner, Michael (1990). Applied Linear Statistical Models. Tokyo: Richard D Irwin, Inc. ISBN ... The same data in ordinary least squares are utilised in this example: A simple linear regression model is fit to this data. The ... Consider a simple linear regression model Y = β 0 + β 1 X + ε {\displaystyle Y=\beta _{0}+\beta _{1}X+\varepsilon } , where Y ... Consider a general linear model as defined in the linear regressions article, that is, Y = X β + ε , {\displaystyle \mathbf {Y ...
https://en.wikipedia.org/wiki/Working–Hotelling_procedure
Bibliography of Saskatchewan historyBibliography of Saskatchewan history
410-427 in JSTOR; statistical models Palmer, Howard. The Settlement of the West (1977) online edition Rea, J. E. "The Wheat ...
https://en.wikipedia.org/wiki/Bibliography_of_Saskatchewan_history
GLIM (software)GLIM (software)
... is a statistical software program for fitting generalized linear models (GLMs). It was developed by the Royal Statistical ... Aitkin, Murray (1987). "Modelling Variance Heterogeneity in Normal Regression Using GLIM". Journal of the Royal Statistical ... Whitehead, John (1980). "Fitting Cox's Regression Model to Survival Data using GLIM". Journal of the Royal Statistical Society ... Aitkin, Murray; Anderson, Dorothy; Francis, Brian; Hinde, John (1989). Statistical Modelling in GLIM. Oxford: Oxford University ...
https://en.wikipedia.org/wiki/GLIM_(software)
Canadian PrairiesCanadian Prairies
410-427 in JSTOR; statistical models Palmer, Howard. The Settlement of the West (1977) online edition Pitsula, James M. " ...
https://en.wikipedia.org/wiki/Canadian_Prairies
Bayesian information criterionBayesian information criterion
Information criteria and statistical modeling. Springer. ISBN 978-0-387-71886-6. Giraud, C. (2015). Introduction to high- ... is a criterion for model selection among a finite set of models; the model with the lowest BIC is preferred. It is based, in ... The models being compared need not be nested, unlike the case when models are being compared using an F-test or a likelihood ... It penalizes the complexity of the model where complexity refers to the number of parameters in the model. It is approximately ...
https://en.wikipedia.org/wiki/Bayesian_information_criterion
Ramsey RESET testRamsey RESET test
... and later published in the Journal of the Royal Statistical Society in 1969. Consider the model y ^ = E { y ∣ x } = β x . {\ ... "Some Properties of Tests for Specification Error in a Linear Regression Model". Journal of the American Statistical Association ... Journal of the Royal Statistical Society, Series B. 31 (2): 350-371. JSTOR 2984219. Ramsey, J. B. (1974). "Classical model ... If the null-hypothesis that all γ {\displaystyle \gamma ~} coefficients are zero is rejected, then the model suffers from ...
https://en.wikipedia.org/wiki/Ramsey_RESET_test
Multiple comparisons problemMultiple comparisons problem
1] Kutner, Michael; Nachtsheim, Christopher; Neter, John; Li, William (2005). Applied Linear Statistical Models. pp. 744-745. ... Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a ... Using a statistical test, we reject the null hypothesis if the test is declared significant. We do not reject the null ... It has been argued that if statistical tests are only performed when there is a strong basis for expecting the result to be ...
https://en.wikipedia.org/wiki/Multiple_comparisons_problem
Beryl May DentBeryl May Dent
... recognised the limitations of the analogue models and developed a digital computer model, and associated program, where non- ... Journal of the American Statistical Association. Alexandria: American Statistical Association. 54 (285): 173-205. doi:10.1080/ ... The dependent variable (labeled y) is modeled as having uncertainty or error. Both independent and dependent measurements may ... Ohio: International Association for Statistical Education. pp. 1-4. ISBN 978-1-118-44511-2. Retrieved 27 October 2020. Mark, ...
https://en.wikipedia.org/wiki/Beryl_May_Dent
Computational neuroscienceComputational neuroscience
The interactions of neurons in a small network can be often reduced to simple models such as the Ising model. The statistical ... Single-neuron modeling[edit]. Main article: Biological neuron models. Even single neurons have complex biophysical ... Earlier models of memory are primarily based on the postulates of Hebbian learning. Biologically relevant models such as ... Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907.[16] This model is still ...
https://en.wikipedia.org/wiki/Computational_neuroscientist
Liquefied natural gasLiquefied natural gas
"Statistical Review infographic". Archived from the original on 23 April 2015. Retrieved 17 April 2015.. ... Model of Tanker LNG Rivers, LNG capacity of 135,000 cubic metres. Interior of an LNG cargo tank ...
https://en.wikipedia.org/wiki/Liquefied_natural_gas
Alternative medicineAlternative medicine
Science-based medicine, with its emphasis on controlled study, proof, evidence, statistical significance and safety is being ... and an incorrect model of the anatomy and physiology of internal organs.[8][59][60][61][62][63] ... A commonly cited statistic is that the US National Institute of Health had spent $2.5 billion on investigating alternative ... Snyderman and Weil remarked that by the early twentieth century the Flexner model had helped to create the 20th-century ...
https://en.wikipedia.org/wiki/Integrative_medicine
Quantitative trait locusQuantitative trait locus
... which is a central premise of his model of selection in nature.[5] Later in his career, Castle would refine his model for ... In this method, in a backcross, one may calculate a t-statistic to compare the averages of the two marker genotype groups. For ... are based on a comparison of single QTL models with a model assuming no QTL. For instance in the "interval mapping" method[23] ... Another interest of statistical geneticists using QTL mapping is to determine the complexity of the genetic architecture ...
https://en.wikipedia.org/wiki/Polygenic_inheritance
Rayleigh scatteringRayleigh scattering
McQuarrie, Donald A. (Donald Allan) (2000). Statistical mechanics. Sausalito, Calif.: University Science Books. pp. 62. ISBN ... Rayleigh sky model. *Ricean fading. *Optical phenomenon. *Dynamic light scattering. *Raman scattering ...
https://en.wikipedia.org/wiki/Rayleigh_scattering
Positron emission tomographyPositron emission tomography
"A statistical model for positron emission tomography". Journal of the American Statistical Association. 80 (389): 8-37. doi: ... Statistical, likelihood-based approaches: Statistical, likelihood-based [37][38] iterative expectation-maximization algorithms ... Proceedings Amererican Statistical Computing: 12-18.. *^ Snyder, D.L.; Miller, M.I.; Thomas, L.J.; Politte, D.G. (1987). "Noise ... since more sophisticated models of the scanner Physics can be incorporated into the likelihood model than those used by ...
https://en.m.wikipedia.org/wiki/PET_scan
SensitizationSensitization
Volkow ND, Koob GF, McLellan AT (January 2016). "Neurobiologic Advances from the Brain Disease Model of Addiction". N. Engl. J ... Substance-use disorder: A diagnostic term in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders ( ... Thus, kindling has been suggested as a model for temporal lobe epilepsy in humans, where stimulation of a repetitive type ( ... Morimoto K, Fahnestock M, Racine RJ (2004). "Kindling and status epilepticus models of epilepsy: Rewiring the brain". Prog ...
https://en.m.wikipedia.org/wiki/Sensitization
Level of measurementLevel of measurement
The studentized range and the coefficient of variation are allowed to measure statistical dispersion. All statistical measures ... Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational ... Statistical data type. References[edit]. *^ a b Kirch, Wilhelm, ed. (2008). "Level of Measurement". Encyclopedia of Public ... Central tendency and statistical dispersion[edit]. The mode, median, and arithmetic mean are allowed to measure central ...
https://en.wikipedia.org/wiki/Numerical_data
Health in NepalHealth in Nepal
"Nepal: WHO Statistical Profile" (PDF). who.int. Retrieved 12 September 2016. "world bank". gapminder.org. Retrieved 7 September ... "Saano Dumre Revisited: Changing Models of Illness in a Village of Central Nepal." Contributions to Nepalese Studies 28(2): 155- ...
https://en.wikipedia.org/wiki/Health_in_Nepal
Substance PSubstance P
"Measurement of plasma-derived substance P: biological, methodological, and statistical considerations". Clinical and Vaccine ... Spacefilling model of substance P. Identifiers. Symbol. TAC1. Alt. symbols. TAC2, NKNA. ...
https://en.m.wikipedia.org/wiki/Substance_P
Psychogenic non-epileptic seizurePsychogenic non-epileptic seizure
Diagnostic and statistical manual of mental disorders : DSM-IV. American Psychiatric Association, American Psychiatric ... "A review of functional neurological symptom disorder etiology and the integrated etiological summary model". Journal of ... "Somatic Symptom and Related Disorders", Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association ... According to the Diagnostic and Statistical Manual of Mental Disorders (version 5) the criteria for receiving a diagnosis of ...
https://en.wikipedia.org/wiki/Psychogenic_non-epileptic_seizure
TornadoTornado
Dotzek, Nikolai, Jürgen Grieser, Harold E. Brooks; Grieser; Brooks (2003-03-01). "Statistical modeling of tornado intensity ... Numerical modeling also provides new insights as observations and new discoveries are integrated into our physical ...
https://en.m.wikipedia.org/wiki/Tornadoes
Mann-Whitney U testMann-Whitney U test
ρ statistic[edit]. A statistic called ρ that is linearly related to U and widely used in studies of categorization ( ... Regression model validation. *Mixed effects models. *Simultaneous equations models. *Multivariate adaptive regression splines ( ... Area-under-curve (AUC) statistic for ROC curves[edit]. The U statistic is equivalent to the area under the receiver operating ... Grissom RJ (1994). "Statistical analysis of ordinal categorical status after therapies". Journal of Consulting and Clinical ...
https://en.wikipedia.org/wiki/Mann-Whitney_U_test
Voxel-based morphometryVoxel-based morphometry
Actual statistical analysis by the general linear model, i.e., statistical parametric mapping. ... The outcome of these steps is a statistical parametric map, highlighting all voxels of the brain where intensities (volume or ... All these may confound the statistical analysis and either decrease the sensitivity to true volumetric effects, or increase the ... The usual approach for statistical analysis is mass-univariate (analysis of each voxel separately), but pattern recognition may ...
https://en.m.wikipedia.org/wiki/Voxel-based_morphometry
ChiropracticChiropractic
doctor/model-centered ←. → patient/situation-centered Professional integration: separate and distinct ←. → integrated into ... as opposed to statistical association) between cervical manipulative therapy (CMT) and VAS.[148] There is insufficient evidence ... as a model of accreditation standards with the goal of having credentials portable internationally.[177] Today, there are 18 ... which models the spine as a torsion bar), Nimmo Receptor-Tonus Technique, applied kinesiology (which emphasises "muscle testing ...
https://en.wikipedia.org/wiki/Chiropractors
Species distributionSpecies distribution
... many researchers believe that species distribution models based on statistical analysis, without including ecological models ... Models can integrate the dispersal/migration model, the disturbance model, and abundance model. Species distribution models ( ... Species distribution models include: presence/absence models, the dispersal/migration models, disturbance models, and abundance ... Species distribution modelsEdit. See also: Environmental niche modelling. Species distribution can now be potentially predicted ...
https://en.m.wikipedia.org/wiki/Species_distribution
Computer graphics (computer science)Computer graphics (computer science)
Models of scattering and shading are used to describe the appearance of a surface. In graphics these problems are often studied ... Geometric Modeling and Industrial Geometry Group at Technische Universitat Wien. *The Institute of Computer Graphics and ... Implicit surface modeling - an older subfield which examines the use of algebraic surfaces, constructive solid geometry, etc., ... A modern rendering of the Utah teapot, an iconic model in 3D computer graphics created by Martin Newell in 1975 ...
https://en.wikipedia.org/wiki/Computer_graphics_
Brain tumorBrain tumor
... to in vivo models of cancer and in 2005 reported a long-term survival benefit in an experimental brain tumor animal model.[62][ ... "Central Brain Tumor Registry of the United States, Primary Brain Tumors in the United States, Statistical Report, 2005-2006" ( ... A uni-multivariate statistical analysis in 76 surgically treated adult patients". Surgical Neurology. 44 (3): 208-21, ...
https://en.m.wikipedia.org/wiki/Brain_tumors
Commercial sexual exploitation of childrenCommercial sexual exploitation of children
Still available, with emphasis on use of Third World youths as models; periodic inroads into traffic by foreign police and U.S ... who constituted the final statistical sample, the average age of entry into the market was 15.29. ... Transient identities and locations of pornographers, rapid turnover in children used as models, and parental release forms.. ...
https://en.m.wikipedia.org/wiki/Commercial_sexual_exploitation_of_children
Quantum thermodynamicsQuantum thermodynamics
Consistency with thermodynamics can be employed to verify quantum dynamical models of transport. For example, local models for ... Quantum statistical mechanics. References[edit]. *^ Einstein, Albert. "Über einen die Erzeugung und Verwandlung des Lichtes ... A quantum version of an adiabatic process can be modeled by an externally controlled time dependent Hamiltonian H. (. t. ). {\ ... It differs from quantum statistical mechanics in the emphasis on dynamical processes out of equilibrium. In addition there is a ...
https://en.wikipedia.org/wiki/Quantum_thermodynamics
Refractive indexRefractive index
... δ in terms of a valid physical model for n and κ. By fitting the theoretical model to the measured R or T, or ψ and δ using ... "Statistical Calculation and Development of Glass Properties. Archived from the original on 2007-10-15.. ... "Non-reflecting" crystal model)". Radiophysics and Quantum Electronics. 21 (9): 916-920. doi:10.1007/BF01031726.. ...
https://en.wikipedia.org/wiki/Index_of_refraction
Progesterone receptorProgesterone receptor
Selective ablation of PR-A in a mouse model, resulting in exclusive production of PR-B, unexpectedly revealed that PR-B ... these follow-up studies lacked the sample size and statistical power to make any definitive conclusions, due to the rarity of ... "Steroid receptor induction of gene transcription: a two-step model". Proceedings of the National Academy of Sciences of the ...
https://en.m.wikipedia.org/wiki/Progesterone_receptor
West Texas IntermediateWest Texas Intermediate
U.S. Energy Information Administration - Part of the U.S. Department of Energy, official source of price and other statistical ... Integrated asset modelling. *Petroleum engineering *Reservoir simulation. *Seismic to simulation. *Petroleum geology ...
https://en.wikipedia.org/wiki/West_Texas_Intermediate
Mark and recaptureMark and recapture
There are very elaborate statistical models available for the analysis of these experiments.[15] A simple model which easily ... Natural Resource Modeling 16:465-475 *^ Maunder, M.N. (2001) Integrated Tagging and Catch-at-Age Analysis (ITCAAN). In Spatial ... Royle, J. A.; R. M. Dorazio (2008). Hierarchical Modeling and Inference in Ecology. Elsevier. ISBN 978-1-930665-55-2. .. ... The model also assumes that no marks fall off animals between visits to the field site by the researcher, and that the ...
https://en.wikipedia.org/wiki/Capture-recapture
Observer effect (physics)Observer effect (physics)
Instead, ψ is an abstract mathematical function that contains all the statistical information that an observer can obtain from ... Additional problems related to decoherence arise when the observer is modeled as a quantum system, as well. ...
https://en.wikipedia.org/wiki/Observer_effect_
Occupational health psychologyOccupational health psychology
Statistical methods used include structural equation modeling[47] and hierarchical linear modeling[48] (HLM; also known as ... Job demands-resources model[edit]. An alternative model, the job demands-resources (JD-R) model,[63] grew out of the DCS model ... Demand-control-support model[edit]. The most influential model in OHP research has been the original demand-control model.[1] ... Effort-reward imbalance model[edit]. After the DCS model, the, perhaps, second most influential model in OHP research has been ...
https://en.wikipedia.org/wiki/Occupational_health_psychology
Data compressionData compression
The Burrows-Wheeler transform can also be viewed as an indirect form of statistical modelling.[9] In a further refinement of ... Models of the human ear-brain combination incorporating such effects are often called psychoacoustic models.[23] ... The perceptual models used to estimate what a human ear can hear are generally somewhat different from those used for music. ... Faxin Yu; Hao Luo; Zheming Lu (2010). Three-Dimensional Model Analysis and Processing. Berlin: Springer. p. 47. ISBN ...
https://en.wikipedia.org/wiki/Audio_compression_
Free willFree will
Compatibilist models adhere to models of mind in which mental activity (such as deliberation) can be reduced to physical ... In the philosophy of decision theory, a fundamental question is: From the standpoint of statistical outcomes, to what extent do ... Models of volition have been constructed in which it is seen as a particular kind of complex, high-level process with an ... suggest models that explain the relationship between conscious intention and action. Benjamin Libet's results are quoted[172] ...
https://en.wikipedia.org/wiki/Free_will
Super-KamiokandeSuper-Kamiokande
Beyond the Standard Model. Simulated Large Hadron Collider CMS particle detector data depicting a Higgs boson produced by ... Higher sensitivity was also necessary to obtain high statistical confidence in its results. This led to the construction of ... The proton is assumed to be absolutely stable in the Standard Model. However, the Grand Unified Theories (GUTs) predict that ... this discovery indicates the finite mass of neutrinos and suggests an extension of the Standard Model. Neutrinos oscillate in ...
https://en.wikipedia.org/wiki/Superkamiokande
IBM NotesIBM Notes
The Domino XML Language (DXL) provides XML representations of all data and design resources in the Notes model, allowing any ... but with the addition of many native classes that model the IBM Notes environment, whereas Formula is similar to Lotus 1-2-3 ...
https://en.wikipedia.org/wiki/Lotus_Domino
Statistical ModelingStatistical Modeling
A new class of statistical model allows estimation of key demographic rates based on fish samples from typical monitoring ... The USGS is incorporating different species and aquatic communities into statistical models to begin developing tools that ... USGS studies related to statistical modeling and the Fisheries Program are listed below. ... USGS software related to statistical modeling and the Fisheries Program are listed below. ...
https://www.usgs.gov/ecosystems/fisheries-program/science/statistical-modeling
Models, Statistical | ASPEModels, Statistical | ASPE
Deriving State-level Estimates from Three National Surveys: A Statistical Assessment and State Tabulations Lisa Alecxih and ... and are corroborated by modeling of certain scenarios. Sales of ...
https://aspe.hhs.gov/document-terms/models-statistical?page=6
Statistical Modelling - WikipediaStatistical Modelling - Wikipedia
Statistical Modelling is a bimonthly peer-reviewed scientific journal covering statistical modelling. It is published by SAGE ... CS1 maint: discouraged parameter (link) "Statistical Modelling". 2014 Journal Citation Reports. Web of Science (Science ed.). ... Publications on behalf of the Statistical Modelling Society. The editors-in-chief are Brian D. Marx (Louisiana State University ...
https://en.wikipedia.org/wiki/Statistical_Modelling
Statistical model - WikipediaStatistical model - Wikipedia
... model Response modeling methodology Scientific model Statistical inference Statistical model specification Statistical model ... More generally, statistical models are part of the foundation of statistical inference. Informally, a statistical model can be ... A statistical model is a special class of mathematical model. What distinguishes a statistical model from other mathematical ... A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample ...
https://en.wikipedia.org/wiki/Statistical_model
Statistical Models for Data Analysis | Paolo Giudici | SpringerStatistical Models for Data Analysis | Paolo Giudici | Springer
Presents novel research in the field of statistical models for data analysis Offers statistical solutions for relevant problems ... Offers statistical solutions for relevant problems *Contains explicit derivation of the proposed models as well as their ... Statistical Models for Data Analysis. Editors: Giudici, Paolo, Ingrassia, Salvatore, Vichi, Maurizio (Eds.) ... The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They ...
http://www.springer.com/us/book/9783319000312
161.200 Statistical Models 161200 - Massey University161.200 Statistical Models 161200 - Massey University
The course covers: basic probability and random variables, models for discrete and continuous data, estimation of model ... The theory behind statistical modelling, and its links to practical applications. ... parameters, assessment of goodness-of-fit, model selection, confidence interval and test construction. ... 161.200 Statistical Models (15 credits). The theory behind statistical modelling, and its links to practical applications. The ...
http://www.massey.ac.nz/massey/learning/programme-course/course.cfm?paper_code=161200
Statistical considerations on prognostic models for glioma. - PubMed - NCBIStatistical considerations on prognostic models for glioma. - PubMed - NCBI
The 3 statistical considerations to establishing clinically useful prognostic models are: study design, model building, and ... Here, we review the statistical considerations of how to build and validate prognostic models, explain the models presented in ... Statistical considerations on prognostic models for glioma.. Molinaro AM1, Wrensch MR1, Jenkins RB1, Eckel-Passow JE1. ... During model building, a discovery cohort of patients can be used to choose variables, construct models, and estimate ...
https://www.ncbi.nlm.nih.gov/pubmed/26657835
Statistical evaluation of alternative models of human evolution | PNASStatistical evaluation of alternative models of human evolution | PNAS
Statistical evaluation of alternative models of human evolution. Nelson J. R. Fagundes, Nicolas Ray, Mark Beaumont, Samuel ... Statistical evaluation of alternative models of human evolution. Nelson J. R. Fagundes, Nicolas Ray, Mark Beaumont, Samuel ... models and each time estimated the posterior probability of the three models. We find that the AFREG and MREBIG models are ... Statistical evaluation of alternative models of human evolution. Nelson J. R. Fagundes, Nicolas Ray, Mark Beaumont, Samuel ...
https://www.pnas.org/content/104/45/17614?ijkey=fdbb74e1688560c62662f641f15bc8327e172f1e&keytype2=tf_ipsecsha
Statistical Models by A. C. Davison | WaterstonesStatistical Models by A. C. Davison | Waterstones
Buy Statistical Models by A. C. Davison from Waterstones today! Click and Collect from your local Waterstones or get FREE UK ... Statistical Models - Cambridge Series in Statistical and Probabilistic Mathematics 11 (Paperback). A. C. Davison (author) Sign ... if asked to summarize Statistical Models in a single word, complete would serve as the only plausible answer. Technometrics ... International Statistical Institute. The volume presents a comprehensive treatment of modern parametric statistical inference ...
https://www.waterstones.com/book/statistical-models/a-c-davison/9780521734493
Information Criteria and Statistical Modeling | SpringerLinkInformation Criteria and Statistical Modeling | SpringerLink
Winner of the 2009 Japan Statistical Association Publication Prize. The Akaike information criterion (AIC) derived as an ... Models bioinformatics computer science model selection and evaluation modeling nonlinear modeling statistical modeling ... which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of ... His primary interests are in time series analysis, non-Gaussian nonlinear filtering and statistical modeling. He is the ...
https://link.springer.com/book/10.1007%2F978-0-387-71887-3
Prediction - Common statistical models | CourseraPrediction - Common statistical models | Coursera
Techniques and Models. Linear regression, ANOVA, logistic regression, multiple factor ANOVA Learn online and earn valuable ... explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer ... We model the logic transformation of p as a linear model of the predictors. ... and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This ...
https://www.coursera.org/lecture/mcmc-bayesian-statistics/prediction-P5i9U
Statistical Models and Methods for Biomedical and Technical Systems | SpringerLinkStatistical Models and Methods for Biomedical and Technical Systems | SpringerLink
International Conference on Statistical Models for Biomedical and Technical Systems, this book is comprised of contributions ... Measures of Divergence, Model Selection, and Survival Models. * Discrepancy-Based Model Selection Criteria Using Cross- ... Cox Models, Analyses, and Extensions. * Extended Cox and Accelerated Models in Reliability, with General Censoring and ... An outgrowth of the "International Conference on Statistical Models for Biomedical and Technical Systems," this book is ...
https://link.springer.com/book/10.1007/978-0-8176-4619-6
Comparing Statistical Models to Predict Dengue Fever NotificationsComparing Statistical Models to Predict Dengue Fever Notifications
... heart modelling, cardiovascular and lung dynamics, neurobiology, computational neuroscience, biomechanics, biomedical ... Comparing Statistical Models to Predict Dengue Fever Notifications. Arul Earnest,1,2 Say Beng Tan,1 Annelies Wilder-Smith,3,4 ... Arul Earnest, Say Beng Tan, Annelies Wilder-Smith, and David Machin, "Comparing Statistical Models to Predict Dengue Fever ...
https://www.hindawi.com/journals/cmmm/2012/758674/cta/
Statistical Modelling (Part-time) - University of LeicesterStatistical Modelling (Part-time) - University of Leicester
Modelling Survival - proportional hazards, exponential model, Weibull model, the Cox model, partial likelihood, tied ... Statistical Modelling (Part-time). Module code: MD7467. This module introduces the theory and application of Linear Models and ... How to interpret the results of the statistical modelling?. Linear Models. The week will start with a review of basic ... the fundamentals of defining a purpose for a statistical model and also introduces the concepts of model building and model ...
https://le.ac.uk/2019/md7467
Animal Movement: Statistical Models for Telemetry Data - 1st EditionAnimal Movement: Statistical Models for Telemetry Data - 1st Edition
Statistical Models for Telemetry Data By Mevin B. Hooten. , Devin S. Johnson. , Brett T. McClintock. , Juan M. Morales. ... including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book ... The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement ... He earned a PhD in Statistics at Colorado State University and focuses on the development and application of statistical models ...
https://www.routledge.com/Animal-Movement-Statistical-Models-for-Telemetry-Data/Hooten-Johnson-McClintock-Morales/p/book/9781466582149
Advanced Statistical Modelling (Full-time) - University of LeicesterAdvanced Statistical Modelling (Full-time) - University of Leicester
Modelling Survival - proportional hazards, exponential model, Weibull model, the Cox model, partial likelihood, tied ... Model checking and regression diagnostics for the Cox model. Multilevel Modelling. This module will Introduce you to multilevel ... Advanced Statistical Modelling (Full-time). Module code: MD7444. Survival Analysis. Survival analysis is concerned with data ... modelling for the analysis of hierarchical and repeated measures data for both continuous and binary outcomes. You will have ...
https://le.ac.uk/2019/md7444
Applied Statistical Modeling | SAGE Publications IncApplied Statistical Modeling | SAGE Publications Inc
The set as a whole is designed to serve as a master class in how to apply the most commonly used statistical models with the ... Volume Two: Models for panel data; Time series cross-sectional analysis; Spatial models; Logistic regression ... This new four-volume set on Applied Statistical Modeling brings together seminal articles in the field, selected for their ... Volume One: Control variables; Multicolinearity and variance inflation; Interaction models; Multilevel models ...
https://www.sagepub.com/en-us/cab/applied-statistical-modeling/book237277
Setup in R - Common statistical models | CourseraSetup in R - Common statistical models | Coursera
Techniques and Models. Linear regression, ANOVA, logistic regression, multiple factor ANOVA Learn online and earn valuable ... explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer ... model actually appropriate here as a model between income and infant mortality? ... and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This ...
https://www.coursera.org/lecture/mcmc-bayesian-statistics/setup-in-r-z1Aap
Approaching the customer journey through statistical modelsApproaching the customer journey through statistical models
All standout statistical models are built on strong and relevant data so before you start to think about modelling, it is ... Statistical models have been proven to help organisations make decisions based on predictions across the customer life cycle. ... To find out more on how statistical models can help you offer the best customer journey, please contact our analytics team on ... Approaching the customer journey through more efficient statistical models. Decisions & Credit Risk / 26th May 2015. by Mike ...
https://www.experian.co.uk/blogs/latest-thinking/decisions-and-credit-risk/approaching-customer-journey-efficient-statistical-models/
9780470670057 - GARCH Models Structure, Statistical | eCampus.com9780470670057 - GARCH Models Structure, Statistical | eCampus.com
Statistical Inference and Financial Applications is $104.63. Free shipping on all orders over $35.00. ... 10.2 Threshold GARCH Model.. 10.3 Asymmetric Power GARCH Model.. 10.4 Other Asymmetric GARCH Models.. 10.5 A GARCH Model with ... 1.4 Random Variance Models.. 1.5 Bibliographical Notes.. 1.6 Exercises.. Part I Univariate GARCH Models.. 2 GARCH(p, q) ... GARCH Models Structure, Statistical Inference and Financial Applications. by Francq, Christian; Zakoian, Jean-Michel *ISBN13: ...
http://www.ecampus.com/garch-models-structure-statistical/bk/9780470670057
Bug#690800: ITP: pymc -- Bayesian statistical models and fitting algorithmsBug#690800: ITP: pymc -- Bayesian statistical models and fitting algorithms
Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and ... Bug#690800: ITP: pymc -- Bayesian statistical models and fitting algorithms. *To: Debian Bug Tracking System ,[email protected] ... Subject: Bug#690800: ITP: pymc -- Bayesian statistical models and fitting algorithms. *From: Yaroslav Halchenko ,[email protected] ...
https://lists.debian.org/debian-devel/2012/10/msg00341.html
Human Factors and Statistical Modeling Lab - University of WashingtonHuman Factors and Statistical Modeling Lab - University of Washington
Human Factors and Statistical Modeling Lab. G6 Mechanical Engineering Building (for packages). Box 352650. Seattle, WA 98195- ... The Human Factors and Statistical Modeling Lab. Prof. Linda Ng Boyle University of Washington - Seattle, WA 98195 ...
http://depts.washington.edu/hfsm/chun.php
Statistical Modeling: A Primer - KDnuggetsStatistical Modeling: A Primer - KDnuggets
Its critical to understand that statistical models are simplified representations of reality and theyre all wrong but some of ... Tags: Modeling, Statistical Modeling. Its critical to understand that statistical models are simplified representations of ... Statistical models are stochastic and what we normally use in marketing research. To crib from Wikipedia: "A statistical model ... statistical models. First, its critical to understand that statistical models are simplified representations of reality and, ...
https://www.kdnuggets.com/2017/03/statistical-modeling-primer.html
8451: Statistical Model Research Scientist8451: Statistical Model Research Scientist
KDnuggets Home » Jobs » 8451: Statistical Model Research Scientist ( 16:n33 ) 8451: Statistical Model Research Scientist. ... Position: Statistical Model Research Scientist. _Contact_:. Apply online. or email to [email protected] ... Model validation and calibration *Analyzing data from many sources, and applying statistical methods to gain insights *Driving ... Strong statistical / machine learning background *Solid background researching statistical machine learning methods, especially ...
https://www.kdnuggets.com/jobs/16/09-09-8451-statistical-model-research-scientist.html
Statistical Analysis of Shape - Mixed Models: Theory and Applications - Demidenko - Wiley Online LibraryStatistical Analysis of Shape - Mixed Models: Theory and Applications - Demidenko - Wiley Online Library
Demidenko, E. (2004) Statistical Analysis of Shape, in Mixed Models: Theory and Applications, John Wiley & Sons, Inc., Hoboken ...
http://onlinelibrary.wiley.com/doi/10.1002/0471728438.ch11/summary
Applied linear statistical models: regression, analysis of variance, and ... - John Neter, William Wasserman - Google BooksApplied linear statistical models: regression, analysis of variance, and ... - John Neter, William Wasserman - Google Books
Implementation of ANOVA model. Topics in analysis of variance - I. Multifactor analysis of variance. Two factor analysis of ... Aptness of model and remedial measures. Topics in regression analysis - I. General regression and correlation analysis. Matrix ... Normal correlation models. Basic analysis of variance. Single - factor analysis of variance. Analysis of factor effects. ... Applied linear statistical models: regression, analysis of variance, and .... John Neter,William Wasserman. Snippet view - 1974 ...
https://books.google.com/books?id=XBXvAAAAMAAJ&q=decision+rule&dq=related:OCLC38021958&output=html_text&source=gbs_word_cloud_r&cad=5
Applied linear statistical models: regression, analysis of variance, and ... - John Neter, William Wasserman - Google BooksApplied linear statistical models: regression, analysis of variance, and ... - John Neter, William Wasserman - Google Books
Implementation of ANOVA model. Topics in analysis of variance - I. Multifactor analysis of variance. Two factor analysis of ... Aptness of model and remedial measures. Topics in regression analysis - I. General regression and correlation analysis. Matrix ... Normal correlation models. Basic analysis of variance. Single - factor analysis of variance. Analysis of factor effects. ... models.html?id=IhfvAAAAMAAJ&utm_source=gb-gplus-shareApplied linear statistical models. ...
https://books.google.com/books?id=IhfvAAAAMAAJ&q=Hence&dq=related:OCLC38021958&source=gbs_word_cloud_r&cad=5
Predictive Modelling Based on Statistical Learning in BiomedicinePredictive Modelling Based on Statistical Learning in Biomedicine
... heart modelling, cardiovascular and lung dynamics, neurobiology, computational neuroscience, biomechanics, biomedical ... Predictive Modelling Based on Statistical Learning in Biomedicine. Olaf Gefeller,1 Benjamin Hofner,2 Andreas Mayr,1,3 and ... P. Bühlmann and T. Hothorn, "Boosting algorithms: regularization, prediction and model fitting," Statistical Science, vol. 22, ... from machine learning to statistical modelling," Methods of Information in Medicine, vol. 53, no. 6, pp. 419-427, 2014. View at ...
https://www.hindawi.com/journals/cmmm/2017/4041736/
Loop Models in 2d Statistical Mechanics - Department of MathematicsLoop Models in 2d Statistical Mechanics - Department of Mathematics
Loop Models in 2d Statistical Mechanics. Loop Models in 2d Statistical Mechanics. Series: Mathematics Colloquium ... Many 2d planar lattice models may be realized as random non-intersecting nested loops. In the $O(n)$ model, each loop has a ...
http://math.arizona.edu/events/9041
Statistical Power in Structural Equation ModelingStatistical Power in Structural Equation Modeling
Kaplan, D. (1995). Statistical power in structural equation modeling. In R. H. Hoyle (ed.), Structural Equation Modeling: ... Statistical Power in Structural Equation Models. David Kaplan. Department of Educational Studies. University of Delaware. The ... Kaplan, D. (1989a). Model modification in covariance structure analysis: Application of the expected parameter change statistic ... The size of the model was also found to affect the power of the test with larger models giving rise to increased power. Finally ...
http://www2.gsu.edu/~mkteer/power.html
InferenceMethodsBayesianEstimationMathematicsMethodologyPredictionDataChapterApproachesTheoryHypothesisEmpiricalAnalysesEstimateParametersStochastic ProcessesAssessmentLikelihoodNonlinear statisticalProcessesDiagnosticsRegression analysisProportionalAnalysis of variaAlgorithmsANOVAPoissonHierarchicalLinearBiomedicalResults of the statisticalSpatialStatisticiansPredictive ModelsMultivariate modelsAssessProbabilitiesApplied Probability ModelsStatisticsMathematical modelMethodologiesMarkovHazardContinuousBinarySimulationApproachPredictionsProbabilistic modelFrameworkNeuralUniversityBookExtensionsSimple regressionComputationalCategorical
Inference16
  • The volume presents a comprehensive treatment of modern parametric statistical inference. (waterstones.com)
  • Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. (routledge.com)
  • The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. (ecampus.com)
  • Part II Statistical Inference. (ecampus.com)
  • Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments ," Econometrica , Econometric Society, vol. 73(4), pages 1351-1365, July. (repec.org)
  • Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments ," Cahiers de recherche 2003-10, Universite de Montreal, Departement de sciences economiques. (repec.org)
  • More generally, statistical models are part of the foundation of statistical inference. (wikipedia.org)
  • The course also gives an introduction to statistical inference, that is how to extract information from data. (uio.no)
  • It is not uncommon, however, to find outliers and influential observations in growth data that heavily affect statistical inference in growth curve models. (booktopia.com.au)
  • This explosion in the application of such models is due to rapid and current development of methodology to carryout statistical inference of complex nonlinear models and improvements in computer power (faster and multiple processors). (ucsb.edu)
  • While there are many tools available for statistical inference that differ in their effectiveness for specific applications, no formal comparisons have been conducted between various software packages. (ucsb.edu)
  • We evaluate three open source software packages commonly used to carry out statistical inference of complex nonlinear models: OpenBUGS, AD Model Builder, and R. To test the strengths and weaknesses of each package, we will bring together experts in all three software packages and apply a common set of ecological models. (ucsb.edu)
  • The model, Persistent Communities by Eigenvector Smoothing (PisCES), combines information across a series of networks, longitudinally, to strengthen the inference for each period. (phys.org)
  • Journal of Statistical Planning and Inference, 141, 972-983. (scirp.org)
  • Lu, W. and Liang, Y. (2006) Empirical Likelihood Inference for Linear Transformation Models. (scirp.org)
  • He is the author of Statistical Foundations of Econometric Modelling (Cambridge, 1986) and, with D. G. Mayo, Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science (Cambridge, 2010). (cambridge.org)
Methods40
  • It is imperative that published models properly detail the study design and methods for both model building and validation. (nih.gov)
  • It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. (coursera.org)
  • The present lecture notes describe stochastic epidemic models and methods for their statistical analysis. (springer.com)
  • Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. (routledge.com)
  • This truly multi-disciplinary collection covers the most important statistical methods used in sociology, social psychology, political science, management science, media studies, anthropology and human geography. (sagepub.com)
  • In another simulation study with various learning approaches such as random forests, support vector machines, lasso regression, and boosting in combination with a variety of filter methods preselecting features, they investigate Pareto fronts and conclude that it is possible to find models with a stable selection of only a few features without losing much predictive accuracy. (hindawi.com)
  • Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. (routledge.com)
  • Unlike past numerical and statistical analysis methods, we assume that the system under investigation is an unknown, deployed black-box that can be passively observed to obtain sample traces, but cannot be controlled. (psu.edu)
  • About the Book In this book, the author has given some introduction to the nature of complexities and processes within Earth Sciences and some of theses are modeled through probabilistic and statistical methods using linear process. (abebooks.com)
  • These are best exemplified through the use of axiomatic probability using univariate statistical methodology for scalar random variables and multivariate statistical methods for vector random variables. (abebooks.com)
  • This book will also be useful to professional Earth Scientists in formulating models and using statistical methods to make appropriate decisions in their chosen fields. (abebooks.com)
  • 9. Some advanced statistical methods. (abebooks.com)
  • M. La Rocca and C. Perna, Neural network modeling with applications to euro exchange rates,, in Computational Methods in Financial Engineering: Essays in Honour of Manfred Gili , (2008), 163. (aimsciences.org)
  • Computational Methods for Optimizing Manufacturing Technology: Models and Techniques, edited by J. Paulo Davim, IGI Global, 2012, pp. 368-399. (igi-global.com)
  • In J. Davim (Ed.), Computational Methods for Optimizing Manufacturing Technology: Models and Techniques (pp. 368-399). (igi-global.com)
  • This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. (wiley.com)
  • This new statistical analysis improved significantly former results which had been obtained on the previous models with other statistical methods. (numdam.org)
  • Designed to be used in a first course for graduate or upper-level undergraduate students, Basic Statistical Methods and Models builds a practical foundation in the use of statistical tools and imparts a clear understanding of their underlying assumptions and limitations. (booktopia.com.au)
  • The author focuses on applications and the models appropriate to each problem while emphasizing Monte Carlo methods, the Central Limit Theorem, confidence intervals, and power functions. (booktopia.com.au)
  • This, along with its very clear explanations, generous number of exercises, and demonstrations of the extensive uses of statistics in diverse areas applications make Basic Statistical Methods and Models highly accessible to students in a wide range of disciplines. (booktopia.com.au)
  • By the end of the course, students should demonstrate knowledge of the theory underlying linear statistical models, as well as some competence in applying the theory to the analysis of data using R. Students should understand the limitations and implications of key assumptions of linear models, and have a working knowledge of common methods of estimation, hypothesis testing and model diagnostics for linear models. (wustl.edu)
  • Two mathematical models with seven and six parameters have been created for use as methods for identification of the optimum mobile phase in chromatographic separations. (mdpi.com)
  • When you earn your Record of Mastery in Bayesian Statistics, you will have an in-depth and practical understanding of Bayesian methods to build statistical models that incorporate prior judgments or information. (statistics.com)
  • Three methods for estimating the statistical distribution parameters are investigated. (igi-global.com)
  • The second edition of this standard text guides biomedical researchers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. (whsmith.co.uk)
  • An appendix will help the reader select the most appropriate statistical methods for their data. (whsmith.co.uk)
  • 1. What classes of statistical methods are most useful for modeling population activity? (frontiersin.org)
  • 3. How can statistical methods be used to empirically test existing models of (probabilistic) population coding? (frontiersin.org)
  • 4. What role can statistical methods play in formulating novel hypotheses about the principles of information processing in neural populations? (frontiersin.org)
  • Statistical methods play an important role in predicting the efficacy of drugs from clinical study data, based on patient characteristics. (meduniwien.ac.at)
  • These methods can also be used to calculate the range of statistical variation of these predictions. (meduniwien.ac.at)
  • So-called regression models and variable selection methods are used to do this. (meduniwien.ac.at)
  • The recently published research paper describes the design of new statistical prediction methods to be used in the development of new drugs. (meduniwien.ac.at)
  • Methods Six count models (Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), zero-inflated NB (ZINB), hurdle Poisson (HP) and hurdle NB (HNB)) were used to analyse falls count data. (bmj.com)
  • In addition, statistical validation and evaluation methods such as resubstitution, is used in order to establish the interval of confidence for both the error model and the calibration model. (umd.edu)
  • Statistical Models and Methods for Risk. (coursehero.com)
  • Statisticians within UMTRI's Vehicle Safety Analytics, Behavioral Sciences, and the CMISST design statistical methods for the analysis of transportation-related data and provide consulting services covering a broad range of quantitative research. (umich.edu)
  • Now in its second edition, this bestselling textbook offers a comprehensive course in empirical research methods, teaching the probabilistic and statistical foundations that enable the specification and validation of statistical models, providing the basis for an informed implementation of statistical procedure to secure the trustworthiness of evidence. (cambridge.org)
  • This book presents statistical methods - with special focus on including innovative approaches - that allow handling the specificities of ICU data, enabling practitioners to conduct appropriate analyses of their own data. (wiley.com)
  • In many fields of applied studies, there has been increasing interest in developing and implementing Bayesian statistical methods for modelling and data analysis. (oreilly.com)
Bayesian13
  • A Bayesian analysis of the data under this best supported model points to an origin of our species ≈141 thousand years ago (Kya), an exit out-of-Africa ≈51 Kya, and a recent colonization of the Americas ≈10.5 Kya. (pnas.org)
  • One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz's Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. (springer.com)
  • A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach. (springer.com)
  • This course aims to expand our "Bayesian toolbox" with more general models, and computational techniques to fit them. (coursera.org)
  • We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. (coursera.org)
  • http://pymc-devs.github.com/pymc * License : MIT/X Programming Lang: Python Description : Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. (debian.org)
  • Forecasting in large macroeconomic panels using Bayesian Model Averaging ," Staff Reports 163, Federal Reserve Bank of New York. (repec.org)
  • Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging ," Discussion Papers in Economics 04/16, Department of Economics, University of Leicester. (repec.org)
  • It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. (routledge.com)
  • But mostly I was struck by the fact that the Bayesian graphical models used in the analysis were entirely expressed in the plate notation (check Wikipedia): there wasn't a single equation in the presentation. (imstat.org)
  • Can the modern user of Bayesian graphical models read plate diagrams as readily as I can read linear models in matrix form? (imstat.org)
  • A variety of issues on model fittings and model diagnostics are addressed, and many criteria for outlier detection and influential observation identification are created within likelihood and Bayesian frameworks. (booktopia.com.au)
  • In this paper we propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. (archives-ouvertes.fr)
Estimation15
  • This kind of analysis can best be done with detailed mechanistic models, but these models require extensive data and advanced estimation procedures. (usgs.gov)
  • A new class of statistical model allows estimation of key demographic rates based on fish samples from typical monitoring protocols using untagged and unmarked fish. (usgs.gov)
  • The course covers: basic probability and random variables, models for discrete and continuous data, estimation of model parameters, assessment of goodness-of-fit, model selection, confidence interval and test construction. (massey.ac.nz)
  • Are Nonhomogeneous Poisson Process Models Preferable to General-Order Statistics Models for Software Reliability Estimation? (springer.com)
  • The text is divided into two distinct but related parts: modelling and estimation. (springer.com)
  • 6.1 Estimation of ARCH( q ) models by Ordinary Least Squares. (ecampus.com)
  • 6.2 Estimation of ARCH( q ) Models by Feasible Generalized Least Squares. (ecampus.com)
  • 7.2 Estimation of ARMA-GARCH Models by Quasi-Maximum Likelihood. (ecampus.com)
  • For such forecasting, the use of Markov models are not new, but in this paper, an attempt is made to propose a covariate-dependent Markov model to identify the factors that contribute to the estimation of transition probabilities. (jhu.edu)
  • Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. (routledge.com)
  • Model Estimation Using Simulation. (routledge.com)
  • This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis. (statistics.com)
  • This course will teach you the basic theory of linear and non-linear mixed effects models, hierarchical linear models, algorithms used for estimation, primarily for models involving normally distributed errors, and examples of data analysis. (statistics.com)
  • Bickel, P. (1998) Efficient and Adaptive Estimation for Semiparametric Models. (scirp.org)
  • Estimation of patient survival times can be based on a number of statistical models. (oreilly.com)
Mathematics8
  • Genshiro Kitagawa is Director-General of the Institute of Statistical Mathematics and Professor of Statistical Science at the Graduate University for Advanced Study. (springer.com)
  • He is the executive editor of the Annals of the Institute of Statistical Mathematics , co-author of Smoothness Priors Analysis of Time Series, Akaike Information Criterion Statistics, and several Japanese books. (springer.com)
  • An elected fellow of the American Statistical Association and elected member (fellow) of the International Statistical Institute, Professor Hilbe is president of the International Astrostatistics Association, editor-in-chief of two book series, and currently on the editorial boards of six journals in statistics and mathematics. (routledge.com)
  • Free of unwieldy mathematics, Statistical Models for Causal Analysis provides a lucid introduction to statistical models used in the social and biomedical sciences, particularly those models used in the causal analysis of nonexperimental data. (eastwestcenter.org)
  • While omitting a good deal of difficult mathematics, such as derivations of sampling distributions and standard errors, the book nonetheless provides a rigorous and focused examination of model specification and interpretation, illustrating their application to the kinds of research that social and biomedical scientists undertake. (eastwestcenter.org)
  • aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Mathematics and Statistical Modelling. (waset.org)
  • Also, high quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of Mathematics and Statistical Modelling are cordially invited for presentation at the conference. (waset.org)
  • ICMSM 2022 has teamed up with the Special Journal Issue on Mathematics and Statistical Modelling . (waset.org)
Methodology7
  • an excellent reference book for health researchers who are unfamiliar with details of any statistical methodology. (waterstones.com)
  • He earned his PhD in Statistics at the University of Missouri and focuses on the development of statistical methodology for spatial and spatio-temporal ecological processes. (routledge.com)
  • Regardless of the modelling methodology employed, the key here is to build the most predictive, accurate models whilst ensuring that the results do not disregard business logic or objectives. (experian.co.uk)
  • The modern term "statistical learning" for this fusion of methodology from different scientific areas could already be found in the scientific literature (see Vapnik [ 1 , 2 ]), but its meaning was slightly different from today. (hindawi.com)
  • During recent years, considerable research has been devoted to exploring this combination of state-of-the-art statistical methodology with machine learning techniques. (hindawi.com)
  • This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole--building energy savings. (osti.gov)
  • We illustrate the methodology by evaluating five baseline models using data from 29 buildings. (osti.gov)
Prediction15
  • During model building, a discovery cohort of patients can be used to choose variables, construct models, and estimate prediction performance via internal validation. (nih.gov)
  • Framework on internal validation for allocating data into training, learning, evaluation, and test sets for the purposes of quantifying prediction performance and variable/model selection. (nih.gov)
  • Note that a causal model can also be used for prediction and how well it predicts is often (but not always) a criterion for judging how good the model is, so this dichotomy is somewhat blurry. (kdnuggets.com)
  • Via statistical learning approaches, interpretable prediction rules leading to accurate forecasts for future or unseen observations can be deduced from potentially high-dimensional data. (hindawi.com)
  • propose a way to select models based on multiple important criteria: prediction accuracy as well as sparsity and stability of the model. (hindawi.com)
  • We could formalize that relationship in a linear regression model, like this: heighti = b0 + b1agei + εi, where b0 is the intercept, b1 is a parameter that age is multiplied by to obtain a prediction of height, εi is the error term, and i identifies the child. (wikipedia.org)
  • A review is given of different ways of estimating the error rate of a prediction rule based on a statistical model. (nih.gov)
  • We focus on the most relevant aspects of these models in a prediction context. (springer.com)
  • Steyerberg E. (2009) Statistical Models for Prediction. (springer.com)
  • In: Clinical Prediction Models. (springer.com)
  • Statistical model for prediction of retrospective exposure to ethylene oxide in an occupational mortality study. (cdc.gov)
  • The first step in building the model was to determine the amount of industrial hygiene data that was available and suitable for use in the development of the exposure prediction model. (cdc.gov)
  • Once developed, the model was subjected to rigid evaluations in an effort to verify that the model can be used reliably in prediction of historical exposures. (cdc.gov)
  • It requires fitting a baseline model to data from a ``training period'' and using the model to predict total electricity consumption during a subsequent ``prediction period. (osti.gov)
  • The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. (osti.gov)
Data89
  • We are collecting empirical temperature climate data to develop local models describing stream temperature and streamflows in headwater streams in Spread Creek, a Tributary to the Upper Snake River, WY. (usgs.gov)
  • Emphasis is on an integrative approach, combining field and laboratory studies to provide data for mathematical models of ecological and evolutionary dynamics. (usgs.gov)
  • The project is integrating downscaled and regionalized climate models (e.g., stream temperature) with riverscape data, fine-scale aquatic species vulnerability assessments, population genetic connectivity, and remotely sensed riparian and aquatic habitat connectivity analyses. (usgs.gov)
  • The papers in this book cover issues related to the development of novel statistical models for the analysis of data. (springer.com)
  • They offer solutions for relevant problems in statistical data analysis and contain the explicit derivation of the proposed models as well as their implementation. (springer.com)
  • The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society. (springer.com)
  • For each of recent published glioma model studies, the original Kaplan-Meier curves on the left-hand side and the validation in TCGA data on the right-hand side. (nih.gov)
  • Using DNA data from 50 nuclear loci sequenced in African, Asian and Native American samples, we show here by extensive simulations that a simple African replacement model with exponential growth has a higher probability (78%) as compared with alternative multiregional evolution or assimilation scenarios. (pnas.org)
  • However, because past demographic events are likely to have greatly affected current patterns of genetic diversity, genetic data are difficult to interpret without a general demographic model that can explain neutral variability ( 3 ). (pnas.org)
  • I highly recommend this book to anyone who is seriously engaged in the statistical analysis of data or in teaching statistics. (waterstones.com)
  • Real-world data often require more sophisticated models to reach realistic conclusions. (coursera.org)
  • How to select an appropriate model given data from a clinical study? (le.ac.uk)
  • How to assess whether a model fits data well? (le.ac.uk)
  • The material also covers the inclusion of different types of covariate data in statistical models and introduces the ideas of statistical interaction and capturing non-linear effects of continuous covariates. (le.ac.uk)
  • This module will Introduce you to multilevel modelling for the analysis of hierarchical and repeated measures data for both continuous and binary outcomes. (le.ac.uk)
  • All standout statistical models are built on strong and relevant data so before you start to think about modelling, it is necessary to extract and verify the relevant data. (experian.co.uk)
  • Once the modelling team has acquired the data, they need to fully understand it and design a development sample. (experian.co.uk)
  • The ideal scenario is where there is a lot of data available to build linear or logistic regression models. (experian.co.uk)
  • Experian's modelling teams have access to high quality data and the experience and skills to use several different modelling methodologies. (experian.co.uk)
  • Sometimes, though, we are able to compare model predictions with real data - predicted sales versus actual sales, for example. (kdnuggets.com)
  • A statistical model is a class of mathematical model , which embodies a set of assumptions concerning the generation of some sample data , and similar data from a larger population . (kdnuggets.com)
  • A statistical model represents, often in considerably idealized form, the data-generating process. (kdnuggets.com)
  • There are other important categorizations as well, for instance between time-series or longitudinal modeling, in which our data span two or more points in time, and cross-sectional modeling, in which we are only have data for one slice in time. (kdnuggets.com)
  • Marketing mix modeling uses time-series data whereas most marketing research surveys are cross sectional. (kdnuggets.com)
  • Some multi-level models fall between these cracks by combining cross-sectional data with time-series or longitudinal data in one model. (kdnuggets.com)
  • Though complex, models for spatial and spatiotemporal data are relevant to specialized corners of marketing research. (kdnuggets.com)
  • Seeking a Research Scientist who will employ skills and experience to improve, create and innovate data-driven modeling approaches for our price and promotion solutions, while anticipating and charting future research needs. (kdnuggets.com)
  • For the first time, recent methodologic research on boosting functional data and on the application of boosting techniques in advanced survival modelling is reviewed. (hindawi.com)
  • In the paper titled "A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data" A. Bommert et al. (hindawi.com)
  • The cross-country data have been employed for the period 1980-2000 for fitting the model. (jhu.edu)
  • The Bank of England has constructed a 'suite of statistical forecasting models' (the 'Suite') providing judgement-free statistical forecasts of inflation and output growth as inputs into the forecasting process, and to offer measures of relevant news in the data. (repec.org)
  • The book provides not only a clear understanding of principles of model construction but also a working knowledge of how to implement these models using real data. (eastwestcenter.org)
  • Supported by numerous tables and graphs, using real survey data, as well as an appendix of computer programs for the statistical packages SAS, BMDP, and LIMDEP, the book is an ideal primer for understanding and using statistical models in analytical work. (eastwestcenter.org)
  • Statistical Tools- R, SPSS , EXCEL,MINITAB I provide a bunch of services on statistics and data analytics. (freelancer.com)
  • Such is the importance of this wealth of data, we have devised a reliable statistical model to enable the courts to evaluate fingerprint evidence within a framework similar to that which underpins DNA evidence. (redorbit.com)
  • The main focus is on the statistical method: choosing the one that is appropriate for your data followed by the model specification and interpretation of the results. (imperial.ac.uk)
  • some familiarity with statistics and SPSS, to at least the level of Introduction to Statistics Using SPSS or Data Management & Statistical Analysis Using SPSS . (imperial.ac.uk)
  • An admissible model must be consistent with all the data points. (wikipedia.org)
  • Thus, a straight line (heighti = b0 + b1agei) cannot be the equation for a model of the data-unless it exactly fits all the data points, i.e. all the data points lie perfectly on the line. (wikipedia.org)
  • The error term, εi, must be included in the equation, so that the model is consistent with all the data points. (wikipedia.org)
  • Other requirements, which seem less central to me, include their being helpful for devising new models, and for programming to analyse data using the models. (imstat.org)
  • A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. (pnas.org)
  • Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. (pnas.org)
  • If the statistical model provides an adequate representation of the expression data for a specific gene, then the corresponding model parameter estimates can provide certain response characteristics for that gene. (pnas.org)
  • For comparison, we have used statistical modeling to look for regularly oscillating profiles within these large data sets. (pnas.org)
  • In this course, you learn how to perform data preparation, exploration, and comprehensive multivariate statistical models such as principal component, exploratory factor, and cluster analyses using five user-friendly, precompiled SAS macro applications. (sas.com)
  • As part of these analyses, you prepare and explore data, select variables, and create diagnostic plots related to model specification and model assumption validation. (sas.com)
  • Conduct simple regression analysis and basic ANOVA tests and assess the applicability of the models used to the data. (southampton.ac.uk)
  • Wang "downloaded quarterly accounting data for all firms in Compustat, the most widely-used dataset in corporate finance that contains data on over 20,000 firms from SEC filings" and looked at the statistical distribution of leading digits in various pieces of financial information. (columbia.edu)
  • To overcome the problem of data snooping, we extend the scheme based on the use of the reality check with modifications apt to compare nested models. (aimsciences.org)
  • Some applications of the proposed procedure to simulated and real data sets show that it allows to select parsimonious neural network models with the highest predictive accuracy. (aimsciences.org)
  • The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. (wiley.com)
  • Second, a thorough statistical framework was developed in order to estimate and compare their performances on data sets. (numdam.org)
  • This book is intended for postgraduates and statisticians whose research involves longitudinal study, multivariate analysis and statistical diagnostics, and also for scientists who analyze longitudinal data and repeated measures. (booktopia.com.au)
  • The authors provide theoretical details on the model fittings and also emphasize the application of growth curve models to practical data analysis, which are reflected in the analysis of practical examples given in each chapter. (booktopia.com.au)
  • The author brings a fresh approach to the understanding of statistical concepts by integrating throughout Minitab software, providing valuable insight into computer simulation and problem-solving techniquesRosenblatt clearly treats the subject matter by carefully wording the explanations and by having readers work with computer-generated data with properties specified by readers. (booktopia.com.au)
  • Furthermore, the model could be used as an instrument in analysis of the quality of experimental data. (mdpi.com)
  • The results obtained by applying the model with six parameters for deviations of rank sums suggest that the data of the experiment no. 8 are questionable. (mdpi.com)
  • In this Mastery Series, you'll choose three courses (out of five) to learn how to apply linear models to all sorts of data - regression for continuous data, then extensions for categorical and count data, as well as more complex data structures like clustered and hierarchical data. (statistics.com)
  • In this Mastery Series (choose 3 of 5 courses), you'll learn regression, and generalized linear models (GLM) extensions of linear models to cover categorical and count data, plus mixed models to cover clustered and hierarchical data. (statistics.com)
  • This Mastery Series is for you if you are a researcher or analyst who needs to construct statistical models of data. (statistics.com)
  • This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models. (statistics.com)
  • This course will teach you regression models for count data, models with a response or dependent variable data in the form of a count or rate, Poisson regression, the foundation for modeling counts, and extensions and modifications to the basic model. (statistics.com)
  • This course will show you how to use R to create statistical models and use them to analyze data. (statistics.com)
  • This paper describes the risk of injury to the rider in a crash using a statistical model based on real-world accident data. (sae.org)
  • Understanding this kind of data requires powerful statistical techniques for capturing the structure of the neural population responses and their relation with external stimuli or behavioral observations. (frontiersin.org)
  • These statistical models are often used to test hypotheses and make inferences about ecological theories and management decisions based on available data. (ucsb.edu)
  • Working directly with NCEAS informatics staff, we will produce a web‐based guide regarding the utility of each package for particular applications that includes annotated model code for each package, the data sets used in the applications, and peer‐reviewed articles. (ucsb.edu)
  • The data derived from these studies will be used for statistical analyses to more accurately predict drug efficacy. (meduniwien.ac.at)
  • This will involve statistical techniques to filter out relevant biomarkers from the plethora of data. (meduniwien.ac.at)
  • Objective To examine the appropriateness of different statistical models in analysing falls count data. (bmj.com)
  • Conclusions Falls count data consisting of a considerable number of zeros can be appropriately modelled by the NB-based regression models, with the HNB model offering the best fit. (bmj.com)
  • The evaluation procedure presented in this paper provides a defensible guideline to appropriately model falls or similar count data with excess zeros. (bmj.com)
  • The model as developed predicted EtO exposures within 1.1 parts per million (ppm) of the validation data set with a standard deviation of 3.7ppm. (cdc.gov)
  • The authors conclude that the model as developed outperformed the panel of industrial hygienists relative to the validation data in terms of both bias and precision. (cdc.gov)
  • Published in the Proceedings of the National Academy of Sciences , the model now gives researchers a tool that extends past observing static networks at a single snapshot in time, which is hugely beneficial since network data are usually dynamic. (phys.org)
  • The model is really flexible, and we are already starting to use it with fMRI data to understand how regions of the brain interconnect and change over time," said Fuchen Liu, a Ph.D. student in the Department of Statistics and Data Science. (phys.org)
  • In this work, we consider statistical diagnostic for general transformation models with right censored data based on empirical likelihood. (scirp.org)
  • Wang, S. , Deng, X. and Zheng, L. (2014) Statistical Diagnosis for General Transformation Model with Right Censored Data Based on Empirical Likelihood. (scirp.org)
  • Li, J.B., Huang, Z.S. and Lian, H. (2013) Empirical Likelihood Influence for General Transformation Models with Right Censored Data. (scirp.org)
  • Dabrowska, D. and Doksum, K. (1988) Partial Likelihood in Transformation Models with Censoring Data. (scirp.org)
  • Just a couple of general comments: (1) Any model that makes probabilistic predictions can be judged on its own terms by comparing to actual data. (andrewgelman.com)
  • The paper is mostly about computation but it has an interesting discussion of some general ideas about how to model this sort of data. (andrewgelman.com)
  • 2) our setup maps directly onto the 2 parameter IRT model from educational testing, about which much is known… In this sense our approach is a little more model-driven than data-driven (i.e., contrast naive MDS or factor analysis or clustering etc). (andrewgelman.com)
  • My experience is that anything too data-driven in this field tends to run into trouble within political science because it while it is one thing to toss more elaborate statistical setups at the roll call data, they tend to lack the clear theoretical underpinnings of the Euclidean spatial voting model. (andrewgelman.com)
  • What behavioral/political assumptions or processes suggest that we ought to do this when we model the data? (andrewgelman.com)
  • Doubt over the trustworthiness of published empirical results is not unwarranted and is often a result of statistical mis-specification: invalid probabilistic assumptions imposed on data. (cambridge.org)
  • Organized in distinct sections which will provide both introduction and advanced understanding according to the level of the reader, this book will prove a valuable resource to either statisticians involved in ICU studies, or ICU physicians who need to model statistical data. (wiley.com)
  • From CT data of 36 skulls and 21 mandibles separate Statistical Shape Models of the anatomical surfaces were created. (spie.org)
Chapter5
  • Some models may be old-fashioned, whilst some others have been further extended or developed so as to better address special research questions presented in each chapter of the book. (novapublishers.com)
  • In this chapter we will consider regression models when the regressand is dichotomous or binary in nature. (oreilly.com)
  • In the previous chapter we considered the linear regression model where the regressand was assumed to be continuous along with the assumption of normality for the error distribution. (oreilly.com)
  • The belief network probability models of Chapter 8 were defined in terms of features. (ubc.ca)
  • The comprehensive scope of the textbook has been expanded by the addition of a new chapter on the Linear Regression and related statistical models. (cambridge.org)
Approaches11
  • A wide variety of modeling approaches are reconciled in the book using a consistent notation. (routledge.com)
  • Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. (routledge.com)
  • In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches. (routledge.com)
  • Our Analysis team is known for solving client and customer problems, employing the most appropriate statistical and mathematical analytic approaches. (kdnuggets.com)
  • During those years at the end of the last century, the understanding of machine learning technology and its potential combination with statistical modelling approaches was in its infancy. (hindawi.com)
  • Statistical approaches to machine translation (MT) have shown themselves to be effective in the last few years. (uni-muenchen.de)
  • Some previous approaches have used brownian Motions with drift for modelling pollen trajectories. (numdam.org)
  • There are two approaches to undergraduate and graduate courses in linear statistical models and experimental design in applied statistics. (nhbs.com)
  • We discuss common statistical models in medical research such as the linear, logistic, and Cox regression model, and also simpler approaches and more flexible extensions, including regression trees and neural networks. (springer.com)
  • We aimed to compare modeling approaches to estimate the individual survival benefit of treatment with either coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI) for patients with complex coronary artery disease. (nih.gov)
  • We've done some work on the logistic regression ("3-parameter Rasch") model , and it might be helpful to see some references to other approaches. (andrewgelman.com)
Theory14
  • The theory behind statistical modelling, and its links to practical applications. (massey.ac.nz)
  • This module introduces the theory and application of Linear Models and Survival Analysis. (le.ac.uk)
  • The module covers all stages in the linear modelling and survival analysis process, from selecting an initial model, through fitting to model checking and then interpretation and communication of the results and at each stage the necessary theory is developed. (le.ac.uk)
  • This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. (ecampus.com)
  • Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. (ecampus.com)
  • The concept of power in statistical theory is defined as the probability of rejecting the null hypothesis given that the null hypothesis is false. (gsu.edu)
  • It is well known that standard asymptotic theory is not applicable or is very unreliable in models with identification problems or weak instruments. (repec.org)
  • As such, a statistical model is "a formal representation of a theory" (Herman Adèr quoting Kenneth Bollen). (wikipedia.org)
  • In particular, statistical and mathematical models are a necessity for developing some sub-disciplines and theories like population genetics and ecology, neutral theory of molecular evolution and biodiversity, and machine-learning techniques for species distribution modeling. (novapublishers.com)
  • This is probably due to the broad range of applications of the Gibbs ensemble theory in equilibrium statistical mechanics whose form is exponential and also due to the usefulness for curve fittings with two parameters tuning. (mdpi.com)
  • This book provides a comprehensive introduction to the theory of growth curve models with an emphasis on statistical diagnostics. (booktopia.com.au)
  • Although many introductory statistics books already exist, too often their focus leans towards theory and few help readers gain effective experience in using a standard statistical software package. (booktopia.com.au)
  • I don't have any unified theory of these models, and I don't really have any good reason to prefer any of these models to any others. (andrewgelman.com)
  • Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. (psu.edu)
Hypothesis8
  • In the context of structural equation modeling, the null hypothesis is defined by the specification of fixed and free elements in relevant parameter matrices of the model equations. (gsu.edu)
  • The null hypothesis is assessed by forming a discrepancy function between the model-implied set of moments (mean vector and/or covariance matrix) and the sample moments. (gsu.edu)
  • Various discrepancy functions can be formed depending on the particular minimization algorithm being used (e.g. maximum likelihood), but the goal remains the same - namely to derive a test statistic that has a known distribution, and then compare the obtained value of the test statistic against tabled values in order to render a decision vis-a-vis the null hypothesis. (gsu.edu)
  • Note that if the null hypothesis is true for that parameter, then the likelihood ratio chi-square for the model would be zero with degrees-of-freedom equaling the degrees-of-freedom of the model. (gsu.edu)
  • If the null hypothesis is false for that parameter, then the likelihood ratio chi-square will be some positive number reflecting the specification error incurred by fixing that parameter to the value chosen in the initial model. (gsu.edu)
  • This number is the noncentrality parameter (NCP) of the noncentral chi-square distribution , which is the distribution of the test statistic when the null hypothesis is false. (gsu.edu)
  • That is, the square of the T-value (in LISREL) or the Wald test (in EQS) can be used to assess the power of an estimated parameter in the model, against a null hypothesis that the value of the parameter is zero. (gsu.edu)
  • All statistical hypothesis tests and all statistical estimators are derived via statistical models. (wikipedia.org)
Empirical7
  • Empirical evaluation of the competing models was performed using model selection criteria and goodness-of-fit through simulation. (bmj.com)
  • Xue, L.G. and Zhu, L.X. (2010) Empirical Likelihood in Nonparametric and Semiparametric Models. (scirp.org)
  • Qin, G. and Jing, B. (2001) Empirical Likelihood for Cox Regression Model under Random Censorship. (scirp.org)
  • He, B. (2006) Application of the Empirical Likelihood Method in Propotional Hazards Model. (scirp.org)
  • Zhou, M. (2005) Empirical Likelihood Analysis of the Rank Estimator for the Censored Accelerated Failure Time Model. (scirp.org)
  • Zheng, M. and Yu, W. (2011) Empirical Likelihood Method for the Multivariate Accelerated Filure Time Models. (scirp.org)
  • Zhou, M., Kim, M. and Bathke, A. (2012) Empirical Likelihood Analysis for the Heteroscedastic Accelerated Failure Time Model. (scirp.org)
Analyses2
  • The statistical analyses showed that the performances of Lagrange Stochastic models were good, but not better than the previous mechanistic models analysed using this new statistical framework. (numdam.org)
  • The Stata statistical software package is again used to perform the analyses, this time employing the much improved version 10 with its intuitive point and click as well as character-based commands. (whsmith.co.uk)
Estimate6
  • A stochastic model, on the other hand, possesses some inherent randomness and we can only estimate the answer. (kdnuggets.com)
  • First, estimate the model of interest. (gsu.edu)
  • Third, re-estimate the initial model with each estimated parameter fixed at their estimated value and choose an "alternative" fixed value for the parameter of interest. (gsu.edu)
  • The proposed model is employed to estimate the transition probabilities, the factors that contribute to transitions in economic performance, and other relevant characteristics. (jhu.edu)
  • Evolutionary divergence of humans from chimpanzees likely occurred some 8 million years ago rather than the 5 million year estimate widely accepted by scientists, a new statistical model suggests. (phys.org)
  • Such modeling techniques, which are widely used in science and commerce, take into account more overall information than earlier processes used to estimate evolutionary history using just a few individual fossil dates, Martin said. (phys.org)
Parameters9
  • Unlike simple procedures such as the t-test or ANOVA wherein alternative hypotheses pertain to only a few parameters, in structural equation modeling there are considerably more parameters. (gsu.edu)
  • The set Θ {\displaystyle \Theta } defines the parameters of the model. (wikipedia.org)
  • In general terms, this approach involves modeling the association of a generic response with a specific experimental variable, for example, timing, cell type, temperature, or drug dosage, using a set of interpretable parameters. (pnas.org)
  • For example, model parameters may describe the magnitude, duration, or timing of the response. (pnas.org)
  • A comprehensive library of input parameters covers nuclear masses, optical model parameters, ground state deformations, discrete levels and decay schemes, level densities, fission barriers (BARFIT), moments of inertia (MOMFIT), and {gamma}-ray strength functions. (osti.gov)
  • The key to this emerging statistical standard is that it uses sensitivities to process and environmental device parameters to holistically model variations around nominal operating points. (edacafe.com)
  • An ensemble formulation for the Gompertz growth function within the framework of statistical mechanics is presented, where the two growth parameters are assumed to be statistically distributed. (mdpi.com)
  • These models are based on assumptions about wind directionality, gravity, settling velocity and may integrate other biological or external parameters. (numdam.org)
  • Comparisons with the external parameters were quite good, proving that these models can be used in other environmental conditions. (numdam.org)
Stochastic Processes4
  • The main aim of the module is to provide the students with necessary knowledge of statistics and stochastic processes to carry out simple statistical procedures and to be able to develop simulation and other models widely employed in OR. (southampton.ac.uk)
  • The model is split into two parts: Statistics and Stochastic Processes. (southampton.ac.uk)
  • Stochastic Processes and Models. (southampton.ac.uk)
  • The course gives a basic introduction to probability and the use of probability models to describe random variables and stochastic processes. (uio.no)
Assessment2
  • In the framework of structural equation modeling the assessment of power is complicated. (gsu.edu)
  • Bolboacă SD, Pică EM, Cimpoiu CV, Jäntschi L. Statistical Assessment of Solvent Mixture Models Used for Separation of Biological Active Compounds. (mdpi.com)
Likelihood5
  • Models and likelihood are the backbone of modern statistics. (waterstones.com)
  • 7 Estimating GARCH Models by Quasi-Maximum Likelihood. (ecampus.com)
  • A method for evaluating the power of the likelihood ratio test in structural equation modeling was developed by Satorra and Saris (1985) . (gsu.edu)
  • Consideration of the power associated with the likelihood ratio test (or other asymptotically equivalent tests) led to an approach for conducting model modification. (gsu.edu)
  • The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. (routledge.com)
Nonlinear statistical1
  • His primary research interests are in multivariate analysis, statistical learning, pattern recognition and nonlinear statistical modeling. (springer.com)
Processes2
  • field components may be modeled as narrow band random processes. (ni.com)
  • Today's Significance paper, which publishes in advance of the full study in the Journal of the Royal Statistical Society: Series A later this year, highlights this subjectivity in current processes, calling for changes in the way such key evidence is allowed to be presented. (redorbit.com)
Diagnostics2
  • Jian-Xin Pan is a lecturer in Medical Statistics of Keele University in the U.K. He has published more than twenty papers on growth curve models, statistical diagnostics and linear/non-linear mixed models. (booktopia.com.au)
  • Weissfeld, L.A. (1990) Influence Diagnostics for the Proportional Hazards Model. (scirp.org)
Regression analysis1
  • There are many different models that you can fit including simple linear regression, multiple linear regression, analysis of variance (ANOVA), analysis of covariance (ANCOVA), and binary logistic regression. (analyse-it.com)
Proportional2
  • Clayton, D. and Cuzick, J. (1985) Multivariate Generalizations of the Proportional Hazards Model. (scirp.org)
  • For example, a straightforward approach is a proportional hazards (PH) regression model (Nguyen and Rocke 2002). (oreilly.com)
Analysis of varia1
  • Growth-curve models are generalized multivariate analysis-of-variance models. (booktopia.com.au)
Algorithms7
  • give an overview of recent developments in the evolving area of statistical boosting algorithms. (hindawi.com)
  • Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. (routledge.com)
  • This article discusses several ways of illustrating fundamental concepts in statistical and thermal physics by considering various models and algorithms. (compadre.org)
  • J. Tobochnik and H. Gould, Teaching Statistical Physics by Thinking about Models and Algorithms, Am. J. Phys. (compadre.org)
  • article{ Author = "Jan Tobochnik and Harvey Gould", Title = {Teaching Statistical Physics by Thinking about Models and Algorithms}, Journal = {Am. J. Phys. (compadre.org)
  • This leads to the creation of translation models and search algorithms that dramatically improve translation quality for morphologically rich languages. (uni-muenchen.de)
  • Based on clinical studies, it is possible to use these algorithms to identify relevant biomarkers and to assess the statistical reliability of predictions. (meduniwien.ac.at)
ANOVA2
  • Implementation of ANOVA model. (google.com)
  • This is a 2 day practical PC-based workshop, following on from the introduction, presenting more advanced statistical techniques such as ANOVA, ANCOVA, Multiple Regression, Logistic Regression, ROC Curves and Survival analysis. (imperial.ac.uk)
Poisson2
  • The NB-based regression models (HNB, ZINB, NB) were better performed than the Poisson-based regression models (Poisson, ZIP, HP). (bmj.com)
  • Model accuracy measures and Monte Carlo simulation of goodness-of-fit confirmed the lack of fit of the Poisson-based regression models and demonstrated the best fit for the HNB model with comparable good fit for the ZINB and NB models. (bmj.com)
Hierarchical1
  • This point is relevant even to something as seemingly innocuous as hierarchical modeling or robust fitting. (andrewgelman.com)
Linear15
  • We model the logic transformation of p as a linear model of the predictors. (coursera.org)
  • The selected variables are: growth of industry (indgr), population growth (popgr), labor force growth (lfgr), use of energy (eneruse) We have used both linear regression as well as logistic regression models in this study for different time periods. (jhu.edu)
  • The linear regression model for economic growth is presented here. (jhu.edu)
  • Generalized Linear Models. (routledge.com)
  • Here λ 1 is Thiele's version of our modern expectation E . In their 1932 book on matrices, Turnbull and Aitken used an almost modern matrix notation for linear models, Ax − h = ε . (imstat.org)
  • Multiple subscripts were still used in the early 1960s when I learned linear models. (imstat.org)
  • I accept that this approach does not highlight the conditional independences within the model, but I think that much of the time (as in linear models) this is a side issue. (imstat.org)
  • From some time in the late 19th century until the mid-1930s, words for linear models were replaced by variables with subscripts, which were replaced by matrices. (imstat.org)
  • Perform comprehensive analysis in multiple linear and binary logistic regression models. (sas.com)
  • Multiple linear regression modeling. (sas.com)
  • Restricted cubic splines are used to model non-linear relationships. (whsmith.co.uk)
  • Applied Linear Statistical Models serves that market. (nhbs.com)
  • Applied Linear Statistical Models is the leading text in the market. (nhbs.com)
  • Increasingly, non‐linear and complex models are applied as a tool for improving understanding of ecological systems. (ucsb.edu)
  • A linear model describes the relationship between a continuous response variable and the explanatory variables using a linear function. (analyse-it.com)
Biomedical3
  • Modern biomedical applications of this type of statistical learning are also sketched to provide an overview not only of recent methodologic improvements but also of practical implementation of boosting in answering biomedical research questions. (hindawi.com)
  • Eminently clear and highly practical, Statistical Models for Causal Analysis is essential for social science and biomedical professionals wishing to upgrade their methodological skills and students in need of a challenging, yet simplified treatment, of these useful, versatile models that have become essential tools for the modern researcher in these fields. (eastwestcenter.org)
  • Statistical Modeling for Biomedical Res. (whsmith.co.uk)
Results of the statistical1
  • How to interpret the results of the statistical modelling? (le.ac.uk)
Spatial1
  • Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. (routledge.com)
Statisticians3
  • The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. (coursera.org)
  • Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. (routledge.com)
  • The book is written very rigorously and precisely and I strongly recommend it for statisticians or for applied scientists with some mathematical and statistical background. (booktopia.com.au)
Predictive Models1
  • This is an important step towards improving the reliability of predictive models in precision medicine and assisting the development of individualised treatments. (meduniwien.ac.at)
Multivariate models1
  • The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. (ecampus.com)
Assess4
  • Via external validation, an independent dataset can assess how well the model performs. (nih.gov)
  • This provides readers the information necessary to assess the bias in a study, compare other published models, and determine the model's clinical usefulness. (nih.gov)
  • Thus, for any estimated model, it is a simple matter to look at these indices in relation to tabled values of the noncentral chi-square distribution in order to assess power. (gsu.edu)
  • A series of chromatographic response functions were proposed and implemented in order to assess and validate the models. (mdpi.com)
Probabilities5
  • A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. (nih.gov)
  • Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. (nih.gov)
  • Logistic regression modeling technique was used to clarify the relationship among probabilities of minor, serious, fatal injury risk to the rider, and the influence of risk factors in accidents involving opposing vehicle contact point, motorcycle contact point, opposing vehicle speed, motorcycle speed, relative heading angle of impact, and helmet use. (sae.org)
  • We present a probabilistic extension of logic programs below that allows for both relational probabilistic models and compact descriptions of conditional probabilities. (ubc.ca)
  • A relational probability model ( RPM ) or probabilistic relational model is a model in which the probabilities are specified on the relations, independently of the actual individuals. (ubc.ca)
Applied Probability Models1
Statistics3
  • The reader of these lecture notes could thus have a two-fold purpose in mind: to learn about epidemic models and their statistical analysis, and/or to learn and apply techniques in probability and statistics. (springer.com)
  • He has authored twelve statistics texts, including Logistic Regression Models, two editions of the bestseller Negative Binomial Regression, andtwo editions of Generalized Estimating Equations (with J. Hardin). (routledge.com)
  • Rosenblatt writes for introductory (non-calculus-based) courses in statistics that offer a clear understanding of statistical procedures together with underlying assumptions and limitations. (booktopia.com.au)
Mathematical model1
  • The aim of these examples is to help the student to conceptually appreciate the problem and realistically formulate a simple mathematical model for its solution. (abebooks.com)
Methodologies1
  • Using a statistical approach to timing analysis allows designers to unlock the true potential of smaller process technologies by reducing the pessimism that can rob chip performance in traditional design methodologies. (edacafe.com)
Markov4
  • The main objective of this paper is to demonstrate the utility of Markov models in identifying the role of the selected characteristics in explaining the growth in GDP over time. (jhu.edu)
  • Then a covariate dependent Markov model is used to examine the change in performance in economic growth over time. (jhu.edu)
  • The Markov Reward Model Checker (MRMC) is a software tool for verifying properties over probabilistic models. (psu.edu)
  • Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT? (uni-muenchen.de)
Hazard1
  • Modeling strategies that omit interactions may result in misleading estimates of absolute treatment benefit for individual patients with the potential hazard of suboptimal decision making. (nih.gov)
Continuous4
  • We give a description of a Petri net-based framework for modelling and analysing biochemical pathways, which unifies the qualitative, stochastic and continuous paradigms. (psu.edu)
  • We demonstrate how qualitative descriptions are abstractions over stochastic or continuous descriptions, and show that the stochastic and continuous models approximate each other. (psu.edu)
  • We propose a new statistical approach to analyzing stochastic systems against specifications given in a sublogic of continuous stochastic logic (CSL). (psu.edu)
  • In order to develop control charts from run charts, some understanding of statistical models for both discrete and continuous random variables is required, in particular of the normal or Gaussian statistical model. (safaribooksonline.com)
Binary2
  • Binary logistic regression modeling. (sas.com)
  • Fit a model to a binary response variable. (analyse-it.com)
Simulation5
  • simulation tools based on statistical models are sometimes mistaken for deterministic models by naive users because of their user-friendly interfaces. (kdnuggets.com)
  • For model stability, the authors investigate, analytically and in a simulation study, various stability measures and conclude that the Pearson correlation has the best properties. (hindawi.com)
  • The simulative stochastic model checker MC2 has been inspired by the idea of approx260 M. Heiner, D. Gilbert, and R. Donaldson imative LTL checking of deterministic simulation runs, proposed in [AP. (psu.edu)
  • Recent tool features include time-bounded reachability analysis for uniform CTMDPs and CSL model checking by discrete-event simulation. (psu.edu)
  • The use of the statistical software Minitab is integrated throughout the book, giving readers valuable experience with computer simulation and problem-solving techniques. (booktopia.com.au)
Approach12
  • A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. (springer.com)
  • This power-based approach to model modification was advocated by Kaplan (1990 , with subsequent commentary). (gsu.edu)
  • We offer a novel approach to this problem: instead of focusing on index measures, we develop a model that predicts the entire distribution of party vote-shares and, thus, does not require any index measure. (ssrn.com)
  • Featuring an approach that focuses on model specification and interpretation, this innovative work-designed specifically for students and professionals in need of a working knowledge of the subject-is a practice-oriented guide to learning how to use these models in analytical work. (eastwestcenter.org)
  • We illustrate our approach by applying it to an extended model of the three stage cascade, which forms the core of the ERK signal transduction pathway. (psu.edu)
  • This user-friendly approach integrates statistical and graphical analysis tools that are available in SAS/STAT and provides complete statistical solutions without writing SAS code or using a point-and-click approach. (sas.com)
  • The thermodynamics approach considered an energy balance among the different cell activities at some fixed time [ 9 ] and a stochastic model incorporating environmental fluctuations was investigated in [ 10 ]. (mdpi.com)
  • In spite of the above studies and interests in the Gompertz function itself, a statistical ensemble approach to the model is still lacking. (mdpi.com)
  • However, models for pollen transport used in aerobiology are often based on the lagrangian Stochastic approach: velocities of pollen grains satisfy stochastic differential equations or Langevin equations and pollen trajectories are obtained by integrating these velocities. (numdam.org)
  • New models based on this approach are introduced. (numdam.org)
  • A common approach is to adopt some low-dimensional equations for a resolved vector and to model the effects of unresolved variables by some kind of noise, the result being a stochastic model. (cea.fr)
  • In this talk I will describe a model reduction approach that uses an optimization procedure to fit a canonical statistical model to an underlying Hamiltonian dynamics. (cea.fr)
Predictions5
  • how do we turn parameter estimates into model predictions? (coursera.org)
  • Statistical models have been proven to help organisations make decisions based on predictions across the customer life cycle. (experian.co.uk)
  • Modelling pollen dispersal is essential to make predictions of cross-pollination rates in various environmental conditions between plants of a cultivated species. (numdam.org)
  • However, statistical predictions are always subject to a certain range of variation. (meduniwien.ac.at)
  • Several metrics were used to characterize the accuracy of the predictions, and in some cases the best--performing model as judged by one metric was not the best performer when judged by another metric. (osti.gov)
Probabilistic model1
  • Most probabilistic model checkers adopt t. (psu.edu)
Framework2
  • Although our framework is based on Petri nets, it can be applied more widely to other formalisms which are used to model and analyse biochemical networks. (psu.edu)
  • Previous and new models were successively analysed using this framework. (numdam.org)
Neural3
  • In this paper, we propose a strategy for the selection of the hidden layer size in feedforward neural network models. (aimsciences.org)
  • Contributions to this Research Topic should advance statistical modeling of neural populations. (frontiersin.org)
  • Developing a new dynamic statistical model to follow neural gene expressions over time is one of the many brain research breakthroughs to happen at Carnegie Mellon. (phys.org)
University3
  • NEW YORK (GenomeWeb) - Using a pan-cancer analysis called allele-specific copy number analysis of tumors (ASCAT), researchers at the Francis Crick Institute, the University of Leuven, and their colleagues developed a new type statistical model, which they were able to use to identify 27 new tumor suppressing genes. (genomeweb.com)
  • Neumann, from Pennsylvania State University, and his team devised and successfully tested a model for establishing the probability of a print belonging to a particular suspect. (redorbit.com)
  • Researchers at Carnegie Mellon University have developed a new dynamic statistical model to visualize changing patterns in networks, including gene expression during developmental periods of the brain. (phys.org)
Book5
  • A student who has absorbed the contents of this book will be well-prepared to face the statistical world and any instructor would be well-advised to consider using it as a text. (waterstones.com)
  • The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. (routledge.com)
  • In both senses, this book is written for people who wish to fit statistical models and understand them. (routledge.com)
  • This book serves as an elementary guide to showcase some statistical and mathematical models that have been applied and used in contemporary ecological or evolutionary research. (novapublishers.com)
  • This book offers an extensive view of Growth Curve Models and a wide range of issues related with statistical diagnosis. (booktopia.com.au)
Extensions6
  • It supports PCTL and CSL model checking, and their reward extensions. (psu.edu)
  • AUSTIN, Texas-(BUSINESS WIRE)-June 4, 2007- The Silicon Integration Initiative's Open Modeling Coalition has finalized the Si2 Effective Current Source Modeling (ECSM) Statistical Extensions specification draft. (edacafe.com)
  • Continuing to build on the popular ECSM format for modeling timing, noise, and power, the addition of the statistical library format extensions makes the ECSM standard the most advanced open modeling format available. (edacafe.com)
  • Additional work on the statistical extensions was provided by Intel, Freescale, and Sun Microsystems after the contribution was made to the OMC. (edacafe.com)
  • The ECSM statistical extensions accurately model the impact of process and environmental variation - a potentially performance depriving problem - which can negate many of the advantages of moving to process nodes at or below 65nm. (edacafe.com)
  • This new ECSM technology will be demonstrated at the Design Automation Conference, June 3-7, in San Diego, CA. Altos Design Automation (Booth #1260) will be demonstrating Variety(tm) which supports the statistical extensions in ECSM. (edacafe.com)
Simple regression1
  • introducing the mathematical formulation and software implementations for fitting simple regression models. (le.ac.uk)
Computational1
  • The role has both strong research, modeling, and computational components. (kdnuggets.com)
Categorical1
  • Pearson, Spearman, Kendall tau-a,b,c and Goodman-Kruskal correlation coefficients were used in order to identify and to quantify the link and its nature (quantitative, categorical, semi-quantitative, both quantitative and categorical) between experimental values and the values estimated by the mathematical models. (mdpi.com)
Medial axis based statistical shape model (MASSM): Applications to 3D prostate segmentation on MRI | IEEE Conference...
Medial axis based statistical shape model (MASSM): Applications to 3D prostate segmentation on MRI | IEEE Conference... (ieeexplore.ieee.org)
Food Access and Cardiovascular Outcomes in Metropolitan Atlanta Census Tracts With Residents at Low Risk and High Risk of...
Food Access and Cardiovascular Outcomes in Metropolitan Atlanta Census Tracts With Residents at Low Risk and High Risk of... (cdc.gov)
Earth's magnetic field - Wikipedia
Earth's magnetic field - Wikipedia (en.wikipedia.org)
Exploring the Spatial Determinants of Late HIV Diagnosis in Texas
Exploring the Spatial Determinants of Late HIV Diagnosis in Texas (cdc.gov)
Examining the effects of birth order on personality | PNAS
Examining the effects of birth order on personality | PNAS (pnas.org)
Genetic dissection of yield traits in super hybrid rice Xieyou9308 using both unconditional and conditional genome-wide...
Genetic dissection of yield traits in super hybrid rice Xieyou9308 using both unconditional and conditional genome-wide... (nature.com)
Estimating County-Level Mortality Rates Using Highly Censored Data From CDC WONDER
Estimating County-Level Mortality Rates Using Highly Censored Data From CDC WONDER (cdc.gov)
The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values | Genetics
The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values | Genetics (genetics.org)
Plus it
Plus it (care.diabetesjournals.org)
Factors Affecting Accuracy From Genomic Selection in Populations Derived From Multiple Inbred Lines: A Barley Case Study |...
Factors Affecting Accuracy From Genomic Selection in Populations Derived From Multiple Inbred Lines: A Barley Case Study |... (genetics.org)
Integrative Bone Metabolomics-Lipidomics Strategy for Pathological Mechanism of Postmenopausal Osteoporosis Mouse Model |...
Integrative Bone Metabolomics-Lipidomics Strategy for Pathological Mechanism of Postmenopausal Osteoporosis Mouse Model |... (nature.com)
Statistical Models for Data Analysis | Paolo Giudici | Springer
Statistical Models for Data Analysis | Paolo Giudici | Springer (springer.com)
Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro, Brazil |...
Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro, Brazil |... (link.springer.com)
How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study | PNAS
How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study | PNAS (pnas.org)
Adherence to a Mediterranean diet and Alzheimer's disease risk in an Australian population | Translational Psychiatry
Adherence to a Mediterranean diet and Alzheimer's disease risk in an Australian population | Translational Psychiatry (nature.com)
Economic models
Economic models (bankofcanada.ca)
Continued warming could transform Greater Yellowstone fire regimes by mid-21st century | PNAS
Continued warming could transform Greater Yellowstone fire regimes by mid-21st century | PNAS (pnas.org)
Likelihood Inference in Non-Linear Term Structure Models: The Importance of the Lower Bound by Martin M. Andreasen, Andrew...
Likelihood Inference in Non-Linear Term Structure Models: The Importance of the Lower Bound by Martin M. Andreasen, Andrew... (papers.ssrn.com)
Essentials of Monte Carlo Simulation - Statistical Methods for Building Simulation Models | Nick T. Thomopoulos | Springer
Essentials of Monte Carlo Simulation - Statistical Methods for Building Simulation Models | Nick T. Thomopoulos | Springer (springer.com)
Opposing Influences of Affective State Valence on Visual Cortical Encoding | Journal of Neuroscience
Opposing Influences of Affective State Valence on Visual Cortical Encoding | Journal of Neuroscience (jneurosci.org)
A General Bayesian Approach to Analyzing Diallel Crosses of Inbred Strains | Genetics
A General Bayesian Approach to Analyzing Diallel Crosses of Inbred Strains | Genetics (genetics.org)
Bayesian Modeling of Haplotype Effects in Multiparent Populations | Genetics
Bayesian Modeling of Haplotype Effects in Multiparent Populations | Genetics (genetics.org)
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features | British...
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features | British... (nature.com)
Frontiers | Recovery of Sentence Production Processes Following Language Treatment in Aphasia: Evidence from Eyetracking |...
Frontiers | Recovery of Sentence Production Processes Following Language Treatment in Aphasia: Evidence from Eyetracking |... (frontiersin.org)
Exploring drivers and dynamics of early boreal forest recovery of heavily disturbed mine sites: a case study from a...
Exploring drivers and dynamics of early boreal forest recovery of heavily disturbed mine sites: a case study from a... (link.springer.com)
Infiltrating monocytes promote brain inflammation and exacerbate neuronal damage after status epilepticus | PNAS
Infiltrating monocytes promote brain inflammation and exacerbate neuronal damage after status epilepticus | PNAS (pnas.org)
Advanced R Statistical Programming and Data Models - Analysis, Machine Learning, and Visualization | Matt Wiley | Springer
Advanced R Statistical Programming and Data Models - Analysis, Machine Learning, and Visualization | Matt Wiley | Springer (springer.com)
Stochastic Epidemic Models and Their Statistical Analysis | Hakan Andersson | Springer
Stochastic Epidemic Models and Their Statistical Analysis | Hakan Andersson | Springer (springer.com)
11th Conference on Bayesian Nonparametrics | Cambridge University Press
11th Conference on Bayesian Nonparametrics | Cambridge University Press (cambridge.org)
Animal Movement: Statistical Models for Telemetry Data - 1st Edition
Animal Movement: Statistical Models for Telemetry Data - 1st Edition (routledge.com)
B Methodological Issues in Developing Community Health Profiles and Performance Indicator Sets Michael A. Stoto | Improving...
B Methodological Issues in Developing Community Health Profiles and Performance Indicator Sets Michael A. Stoto | Improving... (nap.edu)
Machine Learning and Knowledge Discovery in Databases | SpringerLink
Machine Learning and Knowledge Discovery in Databases | SpringerLink (link.springer.com)