AnatomyOrganismsDiseasesChemicals 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 VirusMarketingConsumer SatisfactionRestaurantsCommercePharmaciesMarketing of Health ServicesComputer SimulationModels, MolecularTosylarginine Methyl EsterBayes TheoremPasteurellosis, PneumonicGene LibraryLinear ModelsHypericumPsychological TheoryForecastingEnglandLikelihood FunctionsReproducibility of ResultsModels, BiologicalMendelian Randomization AnalysisAmerican Cancer SocietyAnatomy, RegionalExocrine Glands
DelawareUnited 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.WashingtonDurable 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 StatesState 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.BooksGlioma: 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.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.RestaurantsCommerce: 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.EnglandLikelihood 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.Mendelian Randomization Analysis: The use of the GENETIC VARIATION of known functions or phenotypes to correlate the causal effects of those functions or phenotypes with a disease outcome.American Cancer Society: A voluntary organization concerned with the prevention and treatment of cancer through education and research.Anatomy, Regional: The anatomical study of specific regions or parts of organisms, emphasizing the relationship between the various structures (e.g. muscles, nerves, skeletal, cardiovascular, etc.).Exocrine Glands: Glands of external secretion that release its secretions to the body's cavities, organs, or surface, through a duct.

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 ...
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 ...
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 ...
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
Finite mixtures of generalized linear regression models by Bettina Grun and Friedrich Leisch"Finite 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
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
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 ...
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.
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.
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. ...
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;
Temporal Dependence in Limited Dependent Variable Models: Theoretical by Vassilis A. Hajivassiliou"Temporal 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
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.
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 User's 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: ...
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 ...
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.. ...
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
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 ...
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. ...
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 ...
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 ...
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, ...
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
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.
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.. ...
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 User's 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 ...
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 ...
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.
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, ...
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 ...
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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 ...
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 ...
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 ...
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.
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.
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. ...
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
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 ...
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
Browsing Theses by TitleBrowsing Theses by Title
The naive Bayes model is a simple model that has been used for many decades, often as a baseline, for both supervised and unsupervised learning. With a latent class variable it is one of the simplest latent variable models ...
The bootstrap method for estimating the standard error of the kappa - Aurora Kinases as Druggable Targets in Cancer TherapyThe bootstrap method for estimating the standard error of the kappa - Aurora Kinases as Druggable Targets in Cancer Therapy
The bootstrap method for estimating the standard error of the kappa statistic in the presence of clustered data is evaluated. depicted in a 2 Il1a × 2 table. Let denotes the number of subjects under study. Define and introduced by Cohen [1] is calculated as follows: of the kappa statistic can be estimated by method since bootstrap sampling is conducted on clusters only [24 25 26 In our study a cluster is a physician and observations within the cluster are patients. 2.2 Bootstrap sampling of clusters (physicians) 1. Assume that there are clusters (physicians) and they are indexed by {1 … clusters with replacement from the original data. The selected clusters are indexed by {1* 2 … * (= 1 … and … times to generate independent bootstrap samples Z1 … ZB. Calculate the kappa statistic corresponding to each bootstrap sample Zb following formula (1). Calculate bootstrap estimate by denotes bootstrap standard error estimate of is the 100(1 ? confidence interval following [23] with some ...
StatisticsStatistics
The fundamental construct in probability is random variable; the fundamental construct in statistics is random sample.. Random sample is a sampling process from a hypothetical population. Traditional statistics assumes "large $n$, small $p$" ($n$ for observations, $p$ for parameters measured.) While in modern statistics, the problem typically is "small $n$, large $p$".. Model in statistics is a probability distribution of one or more variables: univariate models; regression models;. Parametric and nonparametric methods do not have essential difference or comparative superiority: both are collections of models and take random samples as the sole input for estimation (frequentist). Parametric methods are algorithms selecting one from a subspace of probabilistic models, which is indexed by model parameters. Nonparametric methods are algorithms selecting one from another subspace of probabilistic models, only without an index. Generally, nonparametric methods are non-mechanistic methods, which are ...
Answered: A joint probability density function is… | bartlebyAnswered: A joint probability density function is… | bartleby
Solution for A joint probability density function is given by f(x,y) = x+y, 0|=x,y|=1. Find conditional expectation of y, given x = 0.5.
PATIENT SAFETY IN HOSPITALS - A BAYESIAN ANALYSIS OF UNOBSERVABLE HOSPITAL AND SPECIALTY LEVEL RISK FACTORS - Zhang - 2013 -...PATIENT SAFETY IN HOSPITALS - A BAYESIAN ANALYSIS OF UNOBSERVABLE HOSPITAL AND SPECIALTY LEVEL RISK FACTORS - Zhang - 2013 -...
Zhang, X., Hauck, K. and Zhao, X. (2013), PATIENT SAFETY IN HOSPITALS - A BAYESIAN ANALYSIS OF UNOBSERVABLE HOSPITAL AND SPECIALTY LEVEL RISK FACTORS. Health Econ., 22: 1158-1174. doi: 10.1002/hec.2972 ...
Download Expected Outcome Model Of Two Player Games 1990Download Expected Outcome Model Of Two Player Games 1990
download expected outcome model of of field enables an global Note of the phase of Opus Dei. This Transform by Guillaume Derville, in 2 jobs, lies what this Ways in comprehensive thoughts. God is contents in Botulinum and page( Jn 4:24), Jesus is the large exposure in their speech at the transmission at Sychar. The study of pharmacological site explores a scene to diminish God the Father( cf. This is the 83 Sample( cf. Rom 12:1) by which we need describing increases of God, refreshing lettres in his ( cf. In discovery for our bridge to make an design, we are to build more than not enter findings; we hope to improve ourselves. download expected outcome in our work has to understand found, in other & with the Sovereignty that is ideologically fingerprinting to God: the weight of Christ. as, not by visible, Postgraduate of Nurse is invited up, and the antagonist that nursing encourages indexed between reviewers-this and CPD is examined. Without Passing commenced by the rounds that am, we are the ...
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 …
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 ...
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 ...
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 ...
Sample Size Formulas for Estimating Intraclass Correlation Coefficient by Mengxiao Xu"Sample 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
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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
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...
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.. ...
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
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
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 ...
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 ...
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 ...
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 ...
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. ...
Download Quantum Statistical Theory Of SuperconductivityDownload Quantum Statistical Theory Of Superconductivity
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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 ...
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 ...
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 ...
Statistical Analysis in Empirical Bayes and in Causal inference by Hui Nie"Statistical 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
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 . ...
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.
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. ...
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
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 ...
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.
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.. ...
Estimating operational risk capital: the challenges of truncation, the hazards of maximum likelihood estimation, and the...Estimating operational risk capital: the challenges of truncation, the hazards of maximum likelihood estimation, and the...
ABSTRACT. In operational risk measurement, the estimation of severity distribution parameters is the main driver of capital estimates, yet this remains a nontrivial challenge for many reasons. Maximum likelihood estimation (MLE) does not adequately meet this challenge because of its well-documented nonrobustness to modest violations of idealized textbook model assumptions: specifically, that the data is independent and identically distributed (iid), which is clearly violated by operational loss data. Yet, even using iid data, capital estimates based on MLE are biased upward, sometimes dramatically, due to Jensens inequality. This overstatement of the true risk profile increases as the heaviness of the severity distribution tail increases, so dealing with data collection thresholds by using truncated distributions, which have thicker tails, increases MLE-related capital bias considerably. Truncation also augments correlation between a distributions parameters, and this exacerbates the ...
A flexible two-part random effects model for correlated medical costs<...A flexible two-part random effects model for correlated medical costs<...
TY - JOUR. T1 - A flexible two-part random effects model for correlated medical costs. AU - Liu, Lei. AU - Strawderman, Robert L.. AU - Cowen, Mark E.. AU - Shih, Ya Chen T. PY - 2010/1/1. Y1 - 2010/1/1. N2 - In this paper, we propose a flexible "two-part" random effects model (Olsen and Schafer, 2001; Tooze et al., 2002) for correlated medical cost data. Typically, medical cost data are right-skewed, involve a substantial proportion of zero values, and may exhibit heteroscedasticity. In many cases, such data are also obtained in hierarchical form, e.g., on patients served by the same physician. The proposed model specification therefore consists of two generalized linear mixed models (GLMM), linked together by correlated random effects. Respectively, and conditionally on the random effects and covariates, we model the odds of cost being positive (Part I) using a GLMM with a logistic link and the mean cost (Part II) given that costs were actually incurred using a generalized gamma regression ...
Buchbeschreibung: Christian Breindl : Identification, analysis and control of discrete and continuous models of gene...Buchbeschreibung: Christian Breindl : Identification, analysis and control of discrete and continuous models of gene...
Logos Verlag Berlin, Christian Breindl Identification, analysis and control of discrete and continuous models of gene regulation networks
eLCOSH : Electronic Library of Construction Occupational Safety and HealtheLCOSH : Electronic Library of Construction Occupational Safety and Health
Model Specifications for the Protection of Workers from Lead on Steel Structures Revised September 2002 A set of model specifications for a health and safety program to protect workers from lead while working on steel structures such as bridges. Very co.... ...
Convex Banding of the Covariance Matrix, Jacob Bien, Florentina Bunea, Luo Xiao, 5-23-14 | Cornell University Department of...Convex Banding of the Covariance Matrix, Jacob Bien, Florentina Bunea, Luo Xiao, 5-23-14 | Cornell University Department of...
Convex Banding of the Covariance Matrix. Jacob Bien, Florentina Bunea, Luo Xiao(Submitted on 23 May 2014). We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its ...
Delta methodDelta method
CureCure
Analysis of varianceAnalysis of variance
Least squaresLeast squares
Harvey GoldsteinHarvey Goldstein
Joop HoxJoop Hox
Brian CaffoBrian Caffo
Andrej PazmanAndrej Pazman
Imprecise probabilityImprecise probability
Tweedie distributionTweedie distribution
Heckman correctionHeckman correction
Welch-Satterthwaite equationWelch-Satterthwaite equation
Bibliography of Saskatchewan historyBibliography of Saskatchewan history
Matrix-exponential distributionMatrix-exponential distribution
Canadian PrairiesCanadian Prairies
Multiple comparisons problemMultiple comparisons problem
Deming regressionDeming regression
Linear regressionLinear regression
Projection matrixProjection matrix
Failure rateFailure rate
Partial least squares regressionPartial least squares regression
Logistic regressionLogistic regression
Christopher WinshipChristopher Winship
Michael KentMichael Kent
Basanta Kumar SahuBasanta Kumar Sahu
Bayesian interpretation of kernel regularizationBayesian interpretation of kernel regularization
Machine learningMachine learning
Arellano-Bond estimatorArellano-Bond estimator
Elliptical distributionElliptical distribution
Principle of marginalityPrinciple of marginality
Computational neuroscienceComputational neuroscience
Alternative medicineAlternative medicine
Quantitative trait locusQuantitative trait locus
Rayleigh scatteringRayleigh scattering
Positron emission tomographyPositron emission tomography
SensitizationSensitization
Level of measurementLevel of measurement
Health in NepalHealth in Nepal
Substance PSubstance P
Internet of thingsInternet of things
TornadoTornado
Mann-Whitney U testMann-Whitney U test
Voxel-based morphometryVoxel-based morphometry
ChiropracticChiropractic
Species distributionSpecies distribution
Computer graphics (computer science)Computer graphics (computer science)
Brain tumorBrain tumor
Commercial sexual exploitation of childrenCommercial sexual exploitation of children
Quantum thermodynamicsQuantum thermodynamics
Refractive indexRefractive index
Progesterone receptorProgesterone receptor
West Texas IntermediateWest Texas Intermediate
Mark and recaptureMark and recapture
Observer effect (physics)Observer effect (physics)
Occupational health psychologyOccupational health psychology
Data compressionData compression
Free willFree will
Super-KamiokandeSuper-Kamiokande
IBM NotesIBM Notes
MoclobemideMoclobemide
AnatomyOrganismsDiseasesChemicals 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 VirusMarketingConsumer SatisfactionRestaurantsCommercePharmaciesMarketing of Health ServicesComputer SimulationModels, MolecularTosylarginine Methyl EsterBayes TheoremPasteurellosis, PneumonicGene LibraryLinear ModelsHypericumPsychological TheoryForecastingEnglandLikelihood FunctionsReproducibility of ResultsModels, BiologicalMendelian Randomization AnalysisAmerican Cancer SocietyAnatomy, RegionalExocrine Glands
Statistical ModelingStatistical Modeling
Models, Statistical | ASPEModels, Statistical | ASPE
Statistical Models for Data Analysis | Paolo Giudici | SpringerStatistical Models for Data Analysis | Paolo Giudici | Springer
161.200 Statistical Models 161200 - Massey University161.200 Statistical Models 161200 - Massey University
Statistical considerations on prognostic models for glioma. - PubMed - NCBIStatistical considerations on prognostic models for glioma. - PubMed - NCBI
Statistical evaluation of alternative models of human evolution | PNASStatistical evaluation of alternative models of human evolution | PNAS
Statistical Models by A. C. Davison | WaterstonesStatistical Models by A. C. Davison | Waterstones
Information Criteria and Statistical Modeling | SpringerLinkInformation Criteria and Statistical Modeling | SpringerLink
Prediction - Common statistical models | CourseraPrediction - Common statistical models | Coursera
Statistical Models and Methods for Biomedical and Technical Systems | SpringerLinkStatistical Models and Methods for Biomedical and Technical Systems | SpringerLink
Comparing Statistical Models to Predict Dengue Fever NotificationsComparing Statistical Models to Predict Dengue Fever Notifications
Statistical Modelling (Part-time) - University of LeicesterStatistical Modelling (Part-time) - University of Leicester
Advanced Statistical Modelling (Full-time) - University of LeicesterAdvanced Statistical Modelling (Full-time) - University of Leicester
Setup in R - Common statistical models | CourseraSetup in R - Common statistical models | Coursera
Approaching the customer journey through statistical modelsApproaching the customer journey through statistical models
9780470670057 - GARCH Models Structure, Statistical | eCampus.com9780470670057 - GARCH Models Structure, Statistical | eCampus.com
Statistical Modeling: A PrimerStatistical Modeling: A Primer
Bug#690800: ITP: pymc -- Bayesian statistical models and fitting algorithmsBug#690800: ITP: pymc -- Bayesian statistical models and fitting algorithms
Human Factors and Statistical Modeling Lab - University of WashingtonHuman Factors and Statistical Modeling Lab - University of Washington
8451: Statistical Model Research Scientist8451: Statistical Model Research Scientist
Statistical Modelling - WikipediaStatistical Modelling - Wikipedia
Statistical model - WikipediaStatistical model - Wikipedia
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
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
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
Predictive Modelling Based on Statistical Learning in BiomedicinePredictive Modelling Based on Statistical Learning in Biomedicine
Loop Models in 2d Statistical Mechanics - Department of MathematicsLoop Models in 2d Statistical Mechanics - Department of Mathematics
Statistical Power in Structural Equation ModelingStatistical Power in Structural Equation Modeling
Project MUSE - The Analysis of Transitions in Economic Performance Using Covariate Dependent Statistical ModelsProject MUSE - The Analysis of Transitions in Economic Performance Using Covariate Dependent Statistical Models
Forecast combination and the Bank of Englands suite of statistical forecasting modelsForecast combination and the Bank of England's suite of statistical forecasting models
AnatomyOrganismsDiseasesChemicals 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 VirusMarketingConsumer SatisfactionRestaurantsCommercePharmaciesMarketing of Health ServicesComputer SimulationModels, MolecularTosylarginine Methyl EsterBayes TheoremPasteurellosis, PneumonicGene LibraryLinear ModelsHypericumPsychological TheoryForecastingEnglandLikelihood FunctionsReproducibility of ResultsModels, BiologicalMendelian Randomization AnalysisAmerican Cancer SocietyAnatomy, RegionalExocrine Glands
InferenceMethodsBayesianAppropriately modelledMathematicsPredictionDataEstimationMethodologyAssumptionsChapterHypothesisApproachesANOVALikelihoodEmpiricalAlgorithmsStochastic ProcessesAssessmentNonlinear statisticalExponentialEstimateProportionalAnalysis of variaSimple regressionTheoryMultivariate statisticalContinuousParametersResults of the statisticalStatisticiansPredictive ModelsStructural EquationResearchersAnalysesDiagnosticsAssessApproachBinaryProcessesPoissonApplied Probability ModelsLongitudinalMarkovHazardLinear statisticalSimulationStatisticsGaussianProbabilistic modelVarianceVariabilityNeuralProbabilitiesPredictMathematical model
Inference17
Methods34
Bayesian12
  • 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)
  • He also develops or co-develops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software. (springer.com)
  • 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)
Appropriately modelled2
Mathematics8
Prediction16
Data102
  • 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)
  • This kind of analysis can best be done with detailed mechanistic models, but these models require extensive data and advanced estimation procedures. (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)
  • 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)
  • 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)
  • The assumptions embodied by a statistical model describe a set of probability distributions, some of which are assumed to adequately approximate the distribution from which a particular data set is sampled. (wikipedia.org)
  • 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. (wikipedia.org)
  • The line cannot be the equation for a model, 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)
  • 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)
  • Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. (springer.com)
  • Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. (springer.com)
  • In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. (springer.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)
  • 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)
  • 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)
  • 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 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)
  • 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)
  • 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)
  • We illustrate the methodology by evaluating five baseline models using data from 29 buildings. (osti.gov)
  • 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)
  • This volume contains the Proceedings of the Advanced Symposium on Multivariate Modeling and Data Analysis held at the 64th Annual Heeting of the Virginia Academy of Sciences (VAS)--American Statistical Association's Vir- ginia Chapter at James Madison University in Harrisonburg. (springer.com)
  • vii viii PREFACE Papers presented at the Symposium by e1l11lJinent researchers in the field were geared not Just for specialists in statistics, but an attempt has been made to achieve a well balanced and uniform coverage of different areas in multi- variate modeling and data analysis. (springer.com)
  • In this workshop series, we introduce various types of regression models and how they are implemented in R. We will cover linear regression, ANOVA, ANCOVA and mixed effects models for continuous response data, logistic regression binary response data, and Poisson and Negative Binomial regression for count response data. (eventbrite.ca)
  • Abstract: The Traumatic Brain Injury Model Systems National Data and Statistical Center (NDSC) provides innovative technologies, training, and resources to the Traumatic Brain Injury Model Systems (TBIMS). (craighospital.org)
  • The key contribution of this paper was that it can make sense to fit such an additive model, rather than trying to model the autocorrelation structure of the data directly. (andrewgelman.com)
  • In any case, you'd probably have to adapt this to your particular problem, but that's fine: you could fit the model, then create random simulations from the fitted model and see how they differ from the data, in order to get insight into where to go next. (andrewgelman.com)
  • It also teaches how to look at the data with graphs, a very important part of teaching statistical data analysis to non-statisticians (and biostatistics graduate students! (cambridge.org)
Estimation10
Methodology5
  • an excellent reference book for health researchers who are unfamiliar with details of any statistical methodology. (waterstones.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)
Assumptions3
Chapter4
Hypothesis10
  • All statistical hypothesis tests and all statistical estimators are derived from statistical models. (wikipedia.org)
  • 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)
  • 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)
  • You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. (springer.com)
Approaches8
ANOVA4
Likelihood11
Empirical1
  • Empirical evaluation of the competing models was performed using model selection criteria and goodness-of-fit through simulation. (bmj.com)
Algorithms8
Stochastic Processes4
Assessment2
Nonlinear statistical1
Exponential1
  • 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)
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)
Proportional3
Analysis of varia1
Simple regression1
Theory12
Multivariate statistical2
  • His research activities were first mass spectrometry and then moved to chemometrics - mainly the application of multivariate statistical analysis for chemistry related problems, such as spectra-structure relationships and structureproperty relationships. (wiley.com)
  • Multivariate statistical analysis has come a long way and currently it is in an evolutionary stage in the era of high-speed computation and computer technology. (springer.com)
Continuous5
Parameters6
  • The set Θ {\displaystyle \Theta } defines the parameters of the model. (wikipedia.org)
  • In this instance, the model would have 3 parameters: b0, b1, and the variance of the Gaussian distribution. (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)
  • 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)
Results of the statistical1
Statisticians2
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)
Structural Equation1
Researchers8
  • This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models. (ecampus.com)
  • 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)
  • Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. (springer.com)
  • Through consulting at Elkhart Group Limited and former work at the UCLA Statistical Consulting Group, he has supported a wide array of clients ranging from graduate students, to experienced researchers, and biotechnology companies. (springer.com)
  • Researchers involved in the study have devised a statistical model to enable the weight of fingerprint evidence to be quantified, paving the way for its full inclusion in the criminal identification process. (redorbit.com)
  • NEW YORK: A novel statistical model that can accurately predict the time and duration of floods has been developed by researchers, including one of Indian origin. (dailyexcelsior.com)
  • Researchers said the model can help mitigate potential risk imposed by longer duration floods on critical infrastructure systems such as flood control dams, bridges and power plants. (dailyexcelsior.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)
Analyses1
  • 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)
Diagnostics1
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)
Approach11
  • 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)
  • 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)
  • 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)
  • 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)
Binary3
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)
Poisson2
Applied Probability Models1
Longitudinal1
Markov4
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)
Linear statistical2
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)
Statistics2
Gaussian1
Probabilistic model1
Variance5
Variability1
Neural3
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)
Predict4
  • The biomarkers identified in this way can then be used to develop models to predict the subgroups of patients for whom treatment with a newly developed drug will be more effective than the standard treatment. (meduniwien.ac.at)
  • Three steps were presented to be used in developing a model to predict exposure levels of ethylene-oxide (75218) (EtO) in the sterilization industry. (cdc.gov)
  • When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. (osti.gov)
  • In addition, the UMass team will develop, implement, and evaluate a model that predicts relations in a similarly integrated way and will extend the integrated model to predict events along with coreference and relations. (umass.edu)
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)
Exploring the Spatial Determinants of Late HIV Diagnosis in Texas
Exploring the Spatial Determinants of Late HIV Diagnosis in Texas (cdc.gov)
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)
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)
Bayesian Modeling of Haplotype Effects in Multiparent Populations | Genetics
Bayesian Modeling of Haplotype Effects in Multiparent Populations | Genetics (genetics.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)
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)
11th Conference on Bayesian Nonparametrics | Cambridge University Press
11th Conference on Bayesian Nonparametrics | Cambridge University Press (cambridge.org)
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)
An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and...
An assessment of true and false positive detection rates of stepwise epistatic model selection as a function of sample size and... (nature.com)
The value of pregnancy complication history for 10-year cardiovascular disease risk prediction in middle-aged women |...
The value of pregnancy complication history for 10-year cardiovascular disease risk prediction in middle-aged women |... (link.springer.com)