## 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)**Statistical** **Modelling**

... is a bimonthly peer-reviewed scientific journal covering

**statistical****modelling**. It is published by SAGE ... "**Statistical****Modelling**". 2014 Journal Citation Reports. Web of Science (Science ed.). Thomson Reuters. 2015. Official website v ... Publications on behalf of the**Statistical****Modelling**Society. The editors-in-chief are Brian D. Marx (Louisiana State University ...**Statistical** **model**

...

**model**Response**modeling**methodology Scientific**model****Statistical**inference**Statistical****model**specification**Statistical****model**... More generally,**statistical****models**are part of the foundation of**statistical**inference. Informally, a**statistical****model**can be ... A**statistical****model**is a special class of mathematical**model**. What distinguishes a**statistical****model**from other mathematical ... A**statistical****model**is a mathematical**model**that embodies a set of**statistical**assumptions concerning the generation of sample ...**Statistical** **model** validation

...

**model**Sensitivity analysis Spurious relationship**Statistical**conclusion validity**Statistical****model**selection**Statistical****model**... After the**statistical****model**is fitted, the validation set is used as a measure of the**model's**error. If the**model**fits well on ...**model**validation is the task of evaluating whether a chosen**statistical****model**is appropriate or not. Oftentimes in**statistical**... To combat this,**model**validation is used to test whether a**statistical****model**can hold up to permutations in the data. This ...**Statistical** **model** specification

In statistics,

**model**specification is part of the process of building a**statistical****model**: specification consists of selecting ... For more on this topic, see**statistical****model**selection. Abductive reasoning Conceptual**model**Data analysis Data transformation ... in a task called**statistical****model**validation. Theoretical understanding can then guide the modification of the**model**in such a ... The statistician Sir David Cox has said, "How [the] translation from subject-matter problem to**statistical****model**is done is ...**Statistical** **Modelling** Society

Statutes

**Statistical****Modelling**Society "**Statistical****Modelling**Society - Executives and Working Groups". Retrieved 15 June 2018 ...**Statistical****Modelling**. The society holds bi-annual elections to elect an Executive Committee. The**Statistical****Modelling**Society ... and applications in**statistical****modelling**; and bring together statisticians working on**statistical****modelling**from various ... will promote**statistical****modelling**as the general framework for the application of**statistical**ideas; promote important ...###### Testing in binary response index **models**

The test

**statistic**is constructed as: c ( β ^ u ′ ) [ ∇ β c ( β ^ u ) V ^ ∇ β c ( β ^ u ) ′ ] − 1 c ( β ^ u ) → d X Q 2 {\ ... This type of**model**is applied in many economic contexts, especially in**modelling**the choice-making behavior. For instance, Y i ... Denote a binary response index**model**as: P [ Y i = 1 ∣ X i ] = G ( X i β ) {\displaystyle P[Y_{i}=1\mid X_{i}]=G(X_{i}\beta ... In most of the cases, it is not easy or even feasible to compute the MLE under the restricted**model**when c ( β ) {\displaystyle ...###### Exponential family random graph **models**

Exponential Random Graph

**Models**(ERGMs) are a family of**statistical****models**for analyzing data from social and other networks. ... To support**statistical**inference on the processes influencing the formation of network structure, a**statistical****model**should ... Alternative**statistical****models**should reflect the uncertainty associated with a given observation, permit inference about the ... The Exponential family is a broad family of**models**for covering many types of data, not just networks. An ERGM is a**model**from ...###### Graphical **model**

Jordan, M. I. (2004). "Graphical

**Models**".**Statistical**Science. 19: 140-155. doi:10.1214/088342304000000026. Ghahramani, Zoubin ... A graphical**model**or probabilistic graphical**model**(PGM) or structured probabilistic**model**is a probabilistic**model**for which a ... Classic machine learning**models**like hidden Markov**models**, neural networks and newer**models**such as variable-order Markov ... A restricted Boltzmann machine is a bipartite generative**model**specified over an undirected graph. The framework of the**models**...###### First-hitting-time **model**

Aaron, S. D.; Stephenson, A. L.; Cameron, D. W.; Whitmore, G. A. (2015). "A

**statistical****model**to predict one-year risk of death ... An interest in the mathematical properties of first-hitting-times and**statistical****models**and methods for analysis of survival ... Whitmore, G. A.; Neufeldt, A. H. (1970). "An application of**statistical****models**in mental health research". Bull. Math. Biophys ... The**model**considers the event that the amount of money reaches 0, representing bankruptcy. The**model**can answer questions such ...###### Proportional hazards **model**

Cox, David R (1972). "Regression

**Models**and Life-Tables". Journal of the Royal**Statistical**Society, Series B. 34 (2): 187-220. ... Proportional hazards**models**are a class of survival**models**in statistics. Survival**models**relate the time that passes, before ... Often there is an intercept term (also called a constant term or bias term) used in regression**models**. The Cox**model**lacks one ... The term Cox regression**model**(omitting proportional hazards) is sometimes used to describe the extension of the Cox**model**to ...###### Mixed **model**

A mixed

**model**, mixed-effects**model**or mixed error-component**model**is a**statistical****model**containing both fixed effects and ...**model**Fixed effects**model**Generalized linear mixed**model**Linear regression Mixed-design analysis of variance Multilevel**model**... non-linear mixed effects**models**, missing data in mixed effects**models**, and Bayesian estimation of mixed effects**models**. Mixed ... Subsequently, mixed**modeling**has become a major area of**statistical**research, including work on computation of maximum ...###### Hurdle **model**

A hurdle

**model**is a class of**statistical****models**where a random variable is**modelled**using two parts, the first which is the ... Hurdle**models**differ from zero-inflated**models**in that zero-inflated**models****model**the zeros using a two-component mixture**model**... Zero-inflated**model**Truncated normal hurdle**model**Cragg, John G. (1971). "Some**Statistical****Models**for Limited Dependent ... and a probit**model**was used to**model**the zeros. The probit part of the**model**was said to**model**the presence of "hurdles" that ...###### IBM alignment **models**

... are a sequence of increasingly complex

**models**used in**statistical**machine translation to train a ... extra fertility**model****Model**4: added relative alignment**model****Model**5: fixed deficiency problem.**Model**6:**Model**4 combined with ... The sequence of the six**models**can be summarized as:**Model**1: lexical translation**Model**2: additional absolute alignment**model**... deficient**models**). IBM**Model**5 reformulates IBM**Model**4 by enhancing the alignment**model**with more training parameters in order ...###### Shortcut **model**

An important question in

**statistical**mechanics is the dependence of**model**behaviour on the dimension of the system. The ... 2 Ising and Potts**models**". Journal of**Statistical**Physics. 50 (1-2): 1-40. Bibcode:1988JSP....50....1A. doi:10.1007/BF01022985 ... Consider the Ising**model**with the Hamiltonian (with N spins) H = − 1 2 ∑ i , j J ( r ( i , j ) ) s i s j {\displaystyle H=-{\ ... The shortcut**model**starts with a network built on a one-dimensional regular lattice. One then adds edges to create shortcuts ...###### Hubbard **model**

Bach, V.; Lieb, E. H.; Solovej, J. P. (1994). "Generalized Hartree-Fock Theory and the Hubbard

**Model**". Journal of**Statistical**... Anderson impurity**model**Bloch's theorem Electronic band structure Solid-state physics Bose-Hubbard**model**t-J**model**Heisenberg ... the behavior of the Hubbard**model**can be qualitatively different from a tight-binding**model**. For example, the Hubbard**model**... The**model**is named for John Hubbard. The Hubbard**model**states that each electron experiences competing forces: one pushes it to ...###### Phase-field **models** on graphs

Luo, Xiyang; Bertozzi, Andrea L. (2017-05-01). "Convergence of the Graph Allen-Cahn Scheme". Journal of

**Statistical**Physics. ... Phase-field**models**on graphs are a discrete analogue to phase-field**models**, defined on a graph. They are used in image analysis ... In analogy to continuum phase-field**models**, where regions with u close to 0 or 1 are**models**for two phases of the material, ... Graph cuts in computer vision Bertozzi, A.; Flenner, A. (2012-01-01). "Diffuse Interface**Models**on Graphs for Classification of ...###### Configuration **model**

Journal of

**Statistical**Physics. 141 (3): 459-475. arXiv:0910.0627. Bibcode:2010JSP...141..459A. doi:10.1007/s10955-010-0056-z. ... As opposed to the Erdős-Rényi**model**, the degree sequence of the configuration**model**is not restricted to have a Poisson ... This feature of the baseline**model**contradicts the known properties of empirical networks, but extensions of the**model**can ... in the configuration**model**(see the page modularity for details). In the DCM (directed configuration**model**), each node is given ...###### Kinetic exchange **models** of markets

Kinetic exchange

**models**are multi-agent dynamic**models**inspired by the**statistical**physics of energy distribution, which try to ... Basic tools used in this type of**modelling**are probabilistic and**statistical**methods mostly taken from the kinetic theory of ... Cordier, S.; Pareschi, L.; Toscani, G. (2005). "On a kinetic**model**for a simple market economy". Journal of**Statistical**Physics ... The main**modelling**effort has been put to introduce the concepts of savings, and taxation in the setting of an ideal gas-like ...###### Bayesian **model** reduction

... is implemented in the

**Statistical**Parametric Mapping toolbox, in the Matlab function spm_log_evidence_ ... for this reduced**model**are rapidly computed from the full**model**using Bayesian**model**reduction. The hypothesis that the ... A full**model**is fitted to data using standard approaches. Hypotheses are then tested by defining one or more 'reduced'**models**... Bayesian**model**reduction is a method for computing the evidence and posterior over the parameters of Bayesian**models**that ...###### Generalized additive **model**

As with any

**statistical****model**it is important to check the**model**assumptions of a GAM. Residual plots should be examined in the ... "Smoothing parameter and**model**selection for general smooth**models**(with discussion)". Journal of the American**Statistical**... in the**model**with such basis expansions we have turned the GAM into a generalized linear**model**(GLM), with a**model**matrix that ... In common with most R**modelling**functions gam expects a**model**formula to be supplied, specifying the**model**structure to fit. ...###### Structural equation **modeling**

**Statistical**method Simultaneous equations

**model**- Type of

**statistical**

**model**Causal map - A network consisting of links or arcs ... Mixture

**model**,lLatent class

**models**[citation needed] Multilevel

**models**, hierarchical

**models**(e.g. people nested in groups)[ ... Fusion validity

**models**item response theory

**models**[citation needed] Latent class

**models**[citation needed] Latent growth

**modeling**... multi-group

**modeling**, longitudinal

**modeling**, partial least squares path

**modeling**, latent growth

**modeling**and hierarchical or ...

###### Multilevel **model**

... random-effects

**models**, random parameter**models**, or split-plot designs) are**statistical****models**of parameters that vary at more ... Multilevel**models**(also known as hierarchical linear**models**, linear mixed-effect**model**, mixed**models**, nested data**models**, ... See further**Model**selection. Multilevel**models**have the same assumptions as other major general linear**models**(e.g., ANOVA, ... Multilevel**models**are a subclass of hierarchical Bayesian**models**, which are general**models**with multiple levels of random ...###### Ziff-Gulari-Barshad **model**

v t e v t e (Chemical physics, Monte Carlo methods,

**Statistical**mechanics, Computational physics, All stub articles, ... The**model**belongs to the universality class of directed percolation. The**model**was modified several times. Ziff RM, Gulari E, ... The**model**consists of three steps: Adsorption of the reacting species CO and O2 The actual reaction step on the surface: CO + O ... The Ziff-Gulari-Barshad (ZGB)**model**is a simple Monte Carlo method for catalytic reactions of oxidation of carbon monoxide to ...###### Maximum-entropy random graph **model**

"The

**statistical**mechanics of networks". arXiv:cond-mat/0405566. van der Hoorn, Pim; Gabor Lippner; Dmitri Krioukov (2017-10-10 ... Maximum-entropy random graph**models**are random graph**models**used to study complex networks subject to the principle of maximum ... as well as the configuration**model**(CM). and soft configuration**model**(SCM) (which each have n {\displaystyle n} local ... producing an exponential random graph**model**(ERGM). Suppose we are building a random graph**model**consisting of a probability ...###### Ising **model**

... is that the Ising

**model**is useful for any**model**of neural function, because a**statistical****model**for neural activity should be ... Ising**model**Swendsen-Wang algorithm t-J**model**Two-dimensional critical Ising**model**Wolff algorithm XY**model**Z N**model**See ... Spin**models**, Exactly solvable**models**,**Statistical**mechanics, Lattice**models**). ... Ward Kuramoto**model**Maximal evenness Order operator Potts**model**(common with Ashkin-Teller**model**) Spin**models**Square-lattice ...###### STAR **model**

**Statistical**Methods and Applications. 21 (3): 335-339. doi:10.1007/s10260-012-0196-1. (Articles lacking in-text citations from ... to say that STAR

**models**nest the SETAR

**model**lacks justification. Unfortunately, whether one should use a SETAR

**model**or a STAR ... The

**models**can be thought of in terms of extension of autoregressive

**models**discussed above, allowing for changes in the

**model**... The

**model**is usually referred to as the STAR(p)

**models**proceeded by the letter describing the transition function (see below) ...

###### Optimality **model**

Journal of

**Statistical**Physics. 162 (5): 1353-1364. doi:10.1007/s10955-015-1412-9. PMC 4761375. PMID 26941467. Mora, Thierry; ... Optimality**modeling**is the**modeling**aspect of optimization theory. It allows for the calculation and visualization of the costs ... The results from Parker's experiment agree with this**model**. One common use of the optimality**model**is in optimal foraging ... In his**model**, Zach predicted the optimal height for crows to drop the whelks. To do this, Zach calculated the total distance ...###### Thurstonian **model**

CAMO Software

**Statistical**Methods. Lee, Michael; Steyvers, Mark; de Young, Mindy; Miller, Brent (2011). "A**Model**-Based Approach ... A Thurstonian**model**is a stochastic transitivity**model**with latent variables for describing the mapping of some continuous ... Thurstonian**models**have been used as an alternative to generalized linear**models**in analysis of sensory discrimination tasks. ... Prior to 1999, Thurstonian**models**were rarely used for**modeling**tasks involving more than 4 options because of the high- ...###### Abelian sandpile **model**

Fey, A.; Levine, L.; Peres, Y. (2010). "Growth Rates and Explosions in Sandpiles". Journal of

**Statistical**Physics. 138 (1-3): ... The Abelian sandpile**model**(ASM) is the more popular name of the original Bak-Tang-Wiesenfeld**model**(BTW). BTW**model**was the ... The extended sandpile**model**is defined nearly exactly the same as the usual sandpile**model**(i.e. the original Bak-Tang- ... A strongly related**model**is the so-called divisible sandpile**model**, introduced by Levine and Peres in 2008, in which, instead ...###### Semiparametric **model**

... a semiparametric

**model**is a**statistical****model**that has parametric and nonparametric components. A**statistical****model**is a ... Semiparametric regression**Statistical****model**Generalized method of moments Bickel, P. J.; Klaassen, C. A. J.; Ritov, Y.; Wellner ... eds.), Encyclopedia of**Statistical**Sciences, Wiley. Oakes, D. (2006), "Semi-parametric**models**", in Kotz, S.; et al. (eds.), ... These**models**often use smoothing or kernels. A well-known example of a semiparametric**model**is the Cox proportional hazards ...**statistical**-dynamical **model**

... type of

**model**, called a**statistical**-dynamical**model**, forecasts the large-scale circulation by solving equations that describe ...**Statistical**relations that predict the track of the storm based on the large-scale conditions are then used to forecast the ... Other articles where**statistical**-dynamical**model**is discussed: tropical cyclone: Landfall forecasts: … ... type of**model**, called a**statistical**-dynamical**model**, forecasts the large-scale circulation by solving equations that describe ...###### Application of **Statistical** **Model** Validation to a Truck Frame

... truck frame finite element

**models**.For the crossmember, modal performance measure sensitivity to variation parameters was deter ... A**statistical****model**validation process was used to determine predictive confidence bounds for mass and natural frequency ...**Statistical**measures were used to compare and test distributions of computer**model**and field outputs with the intent of ... A**statistical****model**validation process was used to determine predictive confidence bounds for mass and natural frequency ...###### Multilevel **Modeling** Using R
| Journal of **Statistical** Software

###### Use Built-In **Statistical** **Modeling** in Tableau | Pluralsight

Linear Regression

**Model**. A linear regression**model**is one of the oldest machine learning algorithms and is used to quantify the ...**Statistical**analysis is a crucial part of any business intelligence function. The demand for statistics-based functionality is ... To implement the linear regression**model**in Tableau, go to the Analytics pane and drag a trend line to the final scatterplot ... The steps for creating the above**statistical**measures are explained in the subsequent sections. We will be using the coffee ...###### A **Statistical** **Model** for Cognitive Selection Bias by Tess Gadwa on Dribbble

A

**Statistical****Model**for Cognitive Selection Bias designed by Tess Gadwa. Connect with them on Dribbble; the global community ... This**model**highlights the special vulnerabilities of highly decentralized communities (FOSS, Twitter, Reddit) to hidden bias. ... Developed, built, and tested a**model**grounded in probability theory that accurately predicts bias and polarization in highly ...###### Case Applications of **Statistical** **Models** in Ecology and Evolution - Nova Science Publishers

... many of which were formulated using

**statistical**and mathematical functions. In particular,**statistical**and mathematical**models**... Home / Shop / Books / Science and Technology / Environmental Sciences / Ecology / Case Applications of**Statistical****Models**in ... This book serves as an elementary guide to showcase some**statistical**and mathematical**models**that have been applied and used in ... Technical aspects of each**statistical****model**are presented in detail to allow readers to follow and apply their own research and ...###### Publication of **statistical** **models**: hands-on workshop (8-November 12, 2021): Hands on Combine · Indico

###### Dr Gareth Peters and Professor Tomoko Matsui to run 3 day workshop on Spatial Temporal **Modelling** | **Statistical** Science - UCL -...

Dr Gareth Peters and Professor Tomoko Matsui to run 3 day workshop on Spatial Temporal

**Modelling**. 23 July 2013 ... A joint workshop co-organised between UCL and the Institute of**Statistical**Mathetmatics in Tokyo will be running from 1-3 ... It will involve several experts from the fields of**statistical**science, signal processing engineering and probability. There ... Dr Gareth Peters and Professor Tomoko Matsui to run 3 day workshop on Spatial Temporal**Modelling**...###### Case-fatality of COPD exacerbations: a meta-analysis and **statistical** **modeling** approach | European Respiratory Society

Case-fatality of COPD exacerbations: a meta-analysis and

**statistical****modeling**approach. M. Hoogendoorn, R.T. Hoogenveen, M.P. ... Case-fatality of COPD exacerbations: a meta-analysis and**statistical****modeling**approach ... Case-fatality of COPD exacerbations: a meta-analysis and**statistical****modeling**approach ... Case-fatality of COPD exacerbations: a meta-analysis and**statistical****modeling**approach ...###### Groundwater levels and other covariates useful for **statistical** **modeling** for the Mississippi River Valley Alluvial aquifer,...

... are used to support

**statistical**and process-based numerical**modeling**. This page represents a collection of groundwater-level ... Groundwater levels and other covariates useful for**statistical****modeling**for the Mississippi River Valley Alluvial aquifer, ... Groundwater levels and other covariates useful for**statistical****modeling**for the Mississippi River Valley Alluvial aquifer, ... are used to support**statistical**and process-based numerical**modeling**. This page represents a collection of groundwater-level ...###### Figure 2 - Recency-Weighted **Statistical** **Modeling** Approach to Attribute Illnesses Caused by 4 Pathogens to Food Sources Using...

**Statistical** Inference for Diagnostic Classification **Models** | Academic Commons

The second part of the thesis focuses on

**statistical**validation of the Q-matrix. The purpose of this study is to provide a ... In particular, we present definitive answers to the learnability of the Q-matrix for one of the most commonly used**models**, the ... Such a non-empirical approach may lead to misspecification of the Q-matrix and substantial lack of**model**fit, resulting in ... In addition, we study the identifiability of diagnostic**model**parameters under a known Q-matrix. The first part of this thesis ...###### Improving Clinical Prediction **Models** with **Statistical** Representation Learning

Xiu, Zidi (2021). Improving Clinical Prediction

**Models**with**Statistical**Representation Learning. Dissertation, Duke University ... This dissertation starts with a systematic review of classical risk prediction**models**in Chapter 1, and discusses the new ... Such scenarios manifest frequently in practice, and they compromise the validity of standard predictive**models**which often ... With the increasing availability of healthcare data and the current rapid development of machine learning**models**, clinical ...###### "**Statistical** **Modeling** of CO2 Flux Data" by Fang He

In the third

**model**, we use a generalized additive**model**(GAM) to analyze the data as a time series indexed by day, with ... The second**model**is a functional linear regression**model**(FLRM), where we use NDVI as the covariate. Both CO2 flux and NDVI are ... In the first**model**, we decompose the CO2 flux data into different components and build a**model**based on the spatial ... Our goals are**modeling**the seasonal dynamics to generate reasonable predictions, and building a**model**using MODIS data to ...###### 2309.01009] **Modeling** single-molecule stretching experiments using **statistical** thermodynamics

Title:

**Modeling**single-molecule stretching experiments using**statistical**thermodynamics. Authors:Michael R. Buche, Jessica M. ... Download a PDF of the paper titled**Modeling**single-molecule stretching experiments using**statistical**thermodynamics, by Michael ... Download a PDF of the paper titled**Modeling**single-molecule stretching experiments using**statistical**thermodynamics, by Michael ... Though the**model**for the stretched molecule might be exactly solvable, including the device in the**model**often prevents ...###### Adsorption of apolipoprotein A-I to biological membranes. A **statistical** mechanical **model** | EPL

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**Statistical** **Modeling** of the Public Broadcasting Corporation's share of TV Royalties | Analysis & Inference

###### PROBLEMS OF **STATISTICAL** INFERENCE FOR BIRTH AND DEATH QUEUEING **MODELS**

###### "**Statistical** **Modeling** of Longitudinal Medical Cost Data" by Shikun Wang

So, for Aim 2, we developed a flexible

**model**to address further challenges such as heteroscedasticity without imposing a cost ... we developed a longitudinal varying coefficient single-index**model**and computational optimization algorithm that is scalable to ... Bivariate penalized spline; Generalized estimating equations; Joint**modeling**; Semiparametric**model**; SEER-Medicare ... So, for Aim 2, we developed a flexible**model**to address further challenges such as heteroscedasticity without imposing a cost ...**Statistical** methods and **modelling**

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**Model**: A policy analysis tool. Feb. The effect of lockout and last drinks laws on non- ... A computer simulation**model**of the District Criminal Court of New South Wales. Jan. ... A simulation**model**of the NSW District Criminal Court with illustrative applications. Jul. ... The revised Group Risk Assessment**Model**(GRAM 2): Assessing risk of reoffending among adults given non-custodial sanctions. Aug ...###### A **statistical** **model** of aggregate fragmentation

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**statistical****model**of fragmentation of aggregates is proposed, based on the stochastic propagation of cracks through the body ... We perform numerical simulations of the**model**for two-dimensional lattice and reveal that the mass distribution for small- and ... theory which explains the detected power law and demonstrate that the overall fragment mass distribution in our**model**agrees ...**Statistical** Business Analyst | Certification Credential | SAS

Exam ID for the SAS

**Statistical**Business Analyst Certification is A00-240. ... Business Analyst is designed for professionals who use SAS/STAT software to conduct and interpret complex**statistical**data ... Predictive**Modeling**Using Logistic Regression. This course covers predictive**modeling**using SAS/STAT® software with emphasis on ... This introductory course is for SAS users who perform**statistical**analyses using SAS/STAT® software. ...**Statistical** Topic **Models** for Multi-Label Document Classification | PUMA

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**Models**for Multi-Label Document Classification. T. Rubin, A. Chambers, P. Smyth, und M. Steyvers. (2011. ) ... In this paper we investigate a class of generative

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**Statistical** Design and Analytic Considerations for N-of-1 Trials (Chapter 4) | Effective Health Care (EHC) Program

The issues discussed include special features of experimental design, data collection strategies, and

**statistical**analysis. ... we discuss key**statistical**issues for n-of-1 trials-trials of one patient treated multiple times with two or more treatments, ... Instead, it will be necessary to have the**statistical****modeling**, including**model**selection,**model**checking, and**model**... Automation of**Statistical****Modeling**and Analysis Procedures. The implementation of the**statistical****modeling**and analysis ...ARIMAPredictSpatialEpidemiologyMathematicalInferenceApproachesPredictiveApproachNumericalEstimationRegression modelsMethodsThesisFactorsWorkshopDataAnalysesAnalysisLinear Regression2021EstimatesObservationsResultsAbstractSubsequentDiagnosticExperimentsEquationsMethodologyEpidemicCommonlyCalculationsTestChapterProbabilityProcedureEffectVariationYearTransmissionApplicationRiskResearchShowsFullSingle

###### ARIMA5

- We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. (who.int)
- The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box-Ljung test. (who.int)
- Katoch and Sidhu (2021) predicted the spread and the final size of the COVID-19 epidemic in India using the ARIMA model. (who.int)
- 2020) predicted the daily confirmed COVID-19 cases for Malaysia using the ARIMA model. (who.int)
- Monthly data sets from 2000 to 2016 were used to construct autoregressive integrated moving average (ARIMA) models to predict dengue incidence for 12 provinces across the study region. (who.int)

###### Predict7

- Statistical relations that predict the track of the storm based on the large-scale conditions are then used to forecast the storm's future position. (britannica.com)
- Our goals are modeling the seasonal dynamics to generate reasonable predictions, and building a model using MODIS data to predict the CO2 flux data at any location. (uwo.ca)
- Doctors can now use a statistical model to predict the best time to do the transplant. (medlineplus.gov)
- To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. (who.int)
- The final models were able to predict dengue incidence from January to December 2016 that concurred with the observation that dengue epidemics occur mostly in rainy seasons. (who.int)
- A range of approaches, including statistical modelling, mathematical modelling and spatial analysis, have been applied to demonstrate relationships between dengue and climate variables and to predict dengue cases and outbreaks. (who.int)
- 12,13 Statistical models that are commonly constructed to predict dengue incidence cannot precisely predict the time and place of a dengue outbreak. (who.int)

###### Spatial4

- For our spatial study, we study three models. (uwo.ca)
- In the first model, we decompose the CO2 flux data into different components and build a model based on the spatial correlations of each component. (uwo.ca)
- Among the three spatial models, the GAM performed best in that it had the lowest out of sample prediction mean square error. (uwo.ca)
- The RST uses 2 forms of empirical Bayesian modeling (nonspatial and spatial) to estimate age-standardized rates and 95% credible intervals for user-specified geographic units. (cdc.gov)

###### Epidemiology1

- Statistical models in epidemiology / David Clayton and Michael Hills. (who.int)

###### Mathematical3

- Since the 20th century, there have been many exciting achievements in the field of ecology and evolution, many of which were formulated using statistical and mathematical functions. (novapublishers.com)
- In particular, statistical and mathematical models are a necessity for developing some sub-disciplines and theories like population genetics and ecology, neutral theory of molecular evolution and biodiversity, and machine-learning techniques for species distribution modeling. (novapublishers.com)
- This book serves as an elementary guide to showcase some statistical and mathematical models that have been applied and used in contemporary ecological or evolutionary research. (novapublishers.com)

###### Inference4

- Identifiability is a prerequisite for statistical inference, such as parameter estimation and hypothesis testing. (columbia.edu)
- iii) dynamically assigning scores and attend to the observed units to derive robust features.By integrating the ideas from representation learning, variational Bayes, causal inference, and contrastive training, this dissertation builds tools for risk modeling frameworks that are robust to various peculiarities of real-world datasets to yield reliable individualized risk evaluations. (duke.edu)
- In Aim 3, to conduct flexible and reliable inference on the estimated cost trajectory, we developed a longitudinal varying coefficient single-index model and computational optimization algorithm that is scalable to baseline feature inference with noise. (tmc.edu)
- The brief lesson is this: too many factors mean too much redundant information which 'contaminates' statistical inference through extra noise. (lu.se)

###### Approaches2

- This dissertation studies novel statistical machine learning approaches for healthcare risk prediction applications in the presence of challenging scenarios, such as rare events, noisy observations, data imbalance, missingness and censoring. (duke.edu)
- Comparing the statistical model to a camera, i.e., a tool to produce an image of reality, we contrast the deductive and predictive (minimal) modeling approaches on a dataset from a corpus study. (lu.se)

###### Predictive2

- A statistical model validation process was used to determine predictive confidence bounds for mass and natural frequency predictions applied to 1) stamped crossmember and 2) truck frame finite element models. (sae.org)
- Such scenarios manifest frequently in practice, and they compromise the validity of standard predictive models which often expect clean and complete data. (duke.edu)

###### Approach3

- Such a non-empirical approach may lead to misspecification of the Q-matrix and substantial lack of model fit, resulting in erroneous interpretation of testing results. (columbia.edu)
- Multistage modeling of leukemia in benzene workers: a simple approach to fitting the 2-stage clonal expansion model. (cdc.gov)
- We advocate an alternative, "deductive modeling" approach that retains a "full" model of variables generated from our research questions and objectives. (lu.se)

###### Numerical2

- Groundwater-level data, in conjunction with attendant metadata and covariates (predictor variables) data, for the Mississippi River Valley alluvial aquifer (MRVA) are used to support statistical and process-based numerical modeling. (usgs.gov)
- We perform numerical simulations of the model for two-dimensional lattice and reveal that the mass distribution for small- and intermediate-size fragments obeys a power law, F(m)∝m[superscript −3/2], in agreement with experimental observations. (le.ac.uk)

###### Estimation2

- Estimation in and discrimination among the various models are considered, followed by a small sampling experiment into the procedures and an example of their application. (who.int)
- If these unobservables are correlated with the observed indicators (change in labor force), then estimation of even the simplest econometric models becomes challenging. (lu.se)

###### Regression models1

- Multivariable Cox Proportional Hazards ( PH ) regression models and survival random forests (SRF) were built to identify the most influential factors. (bvsalud.org)

###### Methods1

- Existing methods of generating small area estimates often require advanced statistical knowledge, programming and coding skills, and extensive computing power. (cdc.gov)

###### Thesis2

- The second part of the thesis focuses on statistical validation of the Q-matrix. (columbia.edu)
- The third part of this thesis is concerned with the identifiability of the diagnostic model parameters when the Q-matrix is correctly specified. (columbia.edu)

###### Factors4

- Risk and protective factors in Parkinson's disease: a simultaneous and prospective study with classical statistical and novel machine learning models. (bvsalud.org)
- No study has prospectively and simultaneously investigated potential risk / protective factors of PD using both classical statistical and novel machine learning analyses. (bvsalud.org)
- The latter may reveal more complex associations and new factors that are undetected by merely linear models . (bvsalud.org)
- A variety of social, political, and economic factors influenced the creation of the first Diagnostic and Statistical Manual of Mental Disorders (DSM) in 1952. (bvsalud.org)

###### Workshop2

- A joint workshop co-organised between UCL and the Institute of Statistical Mathetmatics in Tokyo will be running from 1-3 August 2013. (ucl.ac.uk)
- The AIDS epidemic and its demographic consequences : proceedings of the United Nations/World Health Organization Workshop on Modelling the Demographic Impact of the AIDS Epidemic in Pattern II Countries: Progress to Date and Policies for the Future, New York, 13-15 December 1989. (who.int)

###### Data23

- One important domain is descriptive statistics , which summarizes data using statistical measures of central tendency and dispersion. (pluralsight.com)
- The basic data structures are intended also to serve as interpretability standards for use by statistical software such as described by Asquith and Seanor (2019) and Asquith and others (2019). (usgs.gov)
- We propose a test statistic that measures the consistency of observed data with the proposed Q-matrix. (columbia.edu)
- We present sufficient and necessary conditions under which the model parameters are identifiable from the response data. (columbia.edu)
- This dissertation starts with a systematic review of classical risk prediction models in Chapter 1, and discusses the new opportunities and challenges presented by the big data era. (duke.edu)
- With the increasing availability of healthcare data and the current rapid development of machine learning models, clinical decision support systems have seen new opportunities to improve clinical practice. (duke.edu)
- However, in healthcare risk prediction applications, statistical analysis is not only challenged by data incompleteness and skewed distributions but also the complexity of the inputs. (duke.edu)
- We investigate a more generalized setting of a multi-classification problem with heavily imbalanced data in Chapter 3, from the perspective of causal machine learning to promote sample efficiency and model generalization. (duke.edu)
- The effectiveness and enhanced prediction accuracy are demonstrated through synthetic data and real-world benchmarks compared with state-of-art models. (duke.edu)
- To better handle sophisticated modern health data and alleviate the impact of real-world data challenges, we introduce a self-attention based model to capture helpful information for time-to-event prediction, called Energy-based Latent Self-Attentive Survival Analysis (ELSSA). (duke.edu)
- In the third model, we use a generalized additive model (GAM) to analyze the data as a time series indexed by day, with covariates such as NDVI, latitude, longitude etc. (uwo.ca)
- Model predictions for the change in free energy of adsorption to zwitterionic membrane are in good agreement with previously reported experimental data with liposomes. (edpsciences.org)
- A likelihood ratio or Wald X² test formally tests the hypothesis of whether the model fits the data better than no model. (analyse-it.com)
- It is common to test whether the model fits the data better than the null model with no parameters. (analyse-it.com)
- So, for Aim 2, we developed a flexible model to address further challenges such as heteroscedasticity without imposing a cost data distributional assumption. (tmc.edu)
- This example uses heart disease mortality data for North Carolina census tracts to map the RST output, including reliability of estimates, and demonstrates a subsequent statistical test. (cdc.gov)
- Epidemic models : their structure and relation to data / edited by Denis Mollison. (who.int)
- Models for infectious human diseases : their structure and relation to data / edited by Valerie Isham, Graham Medley. (who.int)
- Multiple imputation is a statistical technique in which missing data are replaced by several sets of plausible, alternative simulated values. (cdc.gov)
- This analysis shows that the 2-stage clonal expansion model can be readily fit to epidemiologic cohort data by using a simple SAS program. (cdc.gov)
- Using quarterly data from 2000 through 2010, we estimated dynamic panel data models for employment and sales in restaurants and bars. (cdc.gov)
- For the county models, we combined data from all counties for which data were available, whether smoke-free or not, and compared the average effect of smoke-free laws in counties that contain smoke-free communities with counties that have no smoke-free communities. (cdc.gov)
- We demonstrated the use of remote sensing data in time-series to develop a model of dengue incidence in the MDR of Viet Nam. (who.int)

###### Analyses1

- For the crossmember, modal performance measure sensitivity to variation parameters was determined using a designed experiment based on a set of 121 finite element model analyses and field tests of 7 hardware samples, A similar experiment based on a set of 8 finite element model analyses and field tests of 8 hardware samples of the truck frame was conducted. (sae.org)

###### Analysis4

- Statistical analysis is a crucial part of any business intelligence function. (pluralsight.com)
- The results and analysis strategies are general in the sense that they can be further extended to other diagnostic models. (columbia.edu)
- This model acts as a starting point for a deeper analysis of consumer and producer behaviour. (lu.se)
- One early impetus for the creation of the DSM lies in the advent of the Census in 1790, a year after George Washington's inauguration, by the marshals of the US judicial districts (United States Census Bureau, 2013) leading to gathering information about each member of society in a format amenable to statistical analysis. (bvsalud.org)

###### Linear Regression1

- The second model is a functional linear regression model (FLRM), where we use NDVI as the covariate. (uwo.ca)

###### 20211

- Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. (who.int)

###### Estimates3

- A random effect model was used to combine all estimates into a weighted average with 95%-confidence interval. (ersjournals.com)
- Accurate and precise estimates of local-level epidemiologic measures are critical to informing policy and program decisions, but they often require advanced statistical knowledge, programming/coding skills, and extensive computing power. (cdc.gov)
- Modelling a future forecast that estimates the regular number of confirmed cases enhances the implementation of rules aimed at controlling the spread of COVID-19. (who.int)

###### Observations1

- A simple SAS software program (SAS Institute, Inc., Cary, North Carolina) was developed for fitting an exact formulation of the 2-stage clonal expansion model accommodating piecewise constant exposures and left and right censoring of observations. (cdc.gov)

###### Results2

###### Abstract1

- ABSTRACT A cluster random sample of 1073 students in the Islamic Republic of Iran (mean age 14.37 years) completed a self-report questionnaire consisting of an exercise measure, selected constructs of Pender's health promotion model and an assessment of stages of behaviour change in exercise. (who.int)

###### Subsequent1

- The steps for creating the above statistical measures are explained in the subsequent sections. (pluralsight.com)

###### Diagnostic4

- Diagnostic classification models (DCM) are an important recent development in educational and psychological testing. (columbia.edu)
- In addition, we study the identifiability of diagnostic model parameters under a known Q-matrix. (columbia.edu)
- We considered all versions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) from the first one published in 1952 to the latest revision published in mid-2013. (bvsalud.org)
- Although the first Diagnostic and Statistical Manual of Mental Disorders (DSM) was not published until 1952, there is a great deal of history and a collection of driving forces that are responsible for its publication. (bvsalud.org)

###### Experiments4

- Analytic relations describing these experiments are valuable, and these relations can be obtained through the statistical thermodynamics of idealized model systems representing the experiments. (arxiv.org)
- Since the specific thermodynamic ensembles manifested by the experiments affect the outcome, primarily for small molecules, the stretching device must be included in the idealized model system. (arxiv.org)
- Experiments with model membrane systems have indicated, however, that membrane cholesterol reduces apo A-I binding to the membrane. (edpsciences.org)
- We develop an analytical theory which explains the detected power law and demonstrate that the overall fragment mass distribution in our model agrees qualitatively with that one observed in experiments. (le.ac.uk)

###### Equations1

- type of model, called a statistical-dynamical model, forecasts the large-scale circulation by solving equations that describe changes in atmospheric pressure, wind, and moisture. (britannica.com)

###### Methodology1

- Particularly, we rigorously adapted his methodology to factor models and CCE technique. (lu.se)

###### Epidemic1

- Statistical forecast models play a role in predicting future epidemic threats, managing of societal, economic, cultural, and public health matters. (who.int)

###### Commonly1

- In particular, we present definitive answers to the learnability of the Q-matrix for one of the most commonly used models, the DINA model, by specifying a set of sufficient conditions under which the Q-matrix is identifiable up to an explicitly defined equivalence class. (columbia.edu)

###### Calculations1

- The asymptotic theories are first demonstrated to be accurate using the freely jointed chain model, and then using molecular dynamics calculations of a single polyethylene chain. (arxiv.org)

###### Test2

- Statistical measures were used to compare and test distributions of computer model and field outputs with the intent of computing confidence bounds for the selected performance statistics. (sae.org)
- Theoretical properties of the test statistic are studied. (columbia.edu)

###### Chapter1

- Some models may be old-fashioned, whilst some others have been further extended or developed so as to better address special research questions presented in each chapter of the book. (novapublishers.com)

###### Probability3

- Developed, built, and tested a model grounded in probability theory that accurately predicts bias and polarization in highly decentralized communities, in consultation with Joe Benson, Ph.D., Professor of Mathematics at Macalester College. (dribbble.com)
- It will involve several experts from the fields of statistical science, signal processing engineering and probability. (ucl.ac.uk)
- Several models for limited dependent variables (variables having a non-negligible probability of exactly equaling zero) are examined. (who.int)

###### Procedure2

- The purpose of this study is to provide a statistical procedure to help decide whether to accept the Q-matrix provided by the experts. (columbia.edu)
- While a minimal adequate model is more parsimonious, its selection procedure is blind to the research aim and may conceal relevant information. (lu.se)

###### Effect2

- How to build a model to estimate the annual effect and seasonal dynamics is a challenging task. (uwo.ca)
- Here we present a statistical mechanical model to account for the effect of cholesterol. (edpsciences.org)

###### Variation1

- Likelihood ratio tests and maximum likelihood estimators are derived for simple models which describe this variation. (dtic.mil)

###### Year1

- In modeling single sites, we treat each year as a multivariate observation. (uwo.ca)

###### Transmission1

- Modelling onchocerciasis transmission and control / Anton Paul Plaisier. (who.int)

###### Application1

- De Clerck, J. and Doerr, D., "Application of Statistical Model Validation to a Truck Frame," SAE Technical Paper 2006-01-0281, 2006, https://doi.org/10.4271/2006-01-0281 . (sae.org)

###### Risk2

- Title : A Statistical Model for Assessing Genetic Susceptibility as a Risk Factor in Multifactorial Diseases: Lessons from Occupational Asthma Personal Author(s) : Demchuk, Eugene;Yucesoy, Berran;Johnson, Victor J.;Andrew, Michael;Weston, Ainsley;Germolec, Dori R.;De Rosa, Christopher T.;Luster, Michael I. (cdc.gov)
- Cox PH models revealed that age, sex , dysthyroidism and diabetes were associated with an increased risk of PD. (bvsalud.org)

###### Research1

- Technical aspects of each statistical model are presented in detail to allow readers to follow and apply their own research and practices. (novapublishers.com)

###### Shows2

###### Full1

- Here, a dual set of asymptotically correct statistical thermodynamic theories are applied to develop accurate approximations for the full model system that includes both the molecule and the device. (arxiv.org)

###### Single1

- We use parametric bootstrap to validate our single location modeling on 55 flux sites. (uwo.ca)