Results 121 to 130 of about 570,704 (178)

An assessment of estimation methods for generalized linear mixed models with binary outcomes. [PDF]

Stat Med, 2013
Capanu M, Gönen M, Begg CB.
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How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models. [PDF]

Front Hum Neurosci, 2017
Nuthmann A, Einhäuser W, Schütz I.
europepmc   +1 more source

A Review of R Packages for Bayesian Model-based Clustering of High-dimensional Multivariate Environmental Exposures. [PDF]

Curr Environ Health Rep
Stephenson BJK, Fu Y.
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Generalized quasi-linear mixed-effects model

Statistical Methods in Medical Research, 2022
The generalized linear mixed model (GLMM) is one of the most common method in the analysis of longitudinal and clustered data in biological sciences. However, issues of model complexity and misspecification can occur when applying the GLMM. To address these issues, we extend the standard GLMM to a nonlinear mixed-effects model based on quasi-linear ...
Yusuke Saigusa, Shinto Eguchi, Osamu Komori   +2 more
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Generalized Linear Mixed Models

2015
This article provides an overview of generalized linear mixed models (GLMMs), how they are fit to data, and the inferences possible when using them. GLMMs are a class of statistical models that handle a wide variety of distributions for the outcome, accommodate nonlinear models, and model correlated data.
Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, Pius Korner-Nievergelt   +5 more
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Model comparison of generalized linear mixed models

Statistics in Medicine, 2005
AbstractGeneralized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing.
Xin-Yuan, Song, Sik-Yum, Lee
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Generalized linear mixed models for meta-analysis

Statistics in Medicine, 1999
We examine two strategies for meta-analysis of a series of 2 x 2 tables with the odds ratio modelled as a linear combination of study level covariates and random effects representing between-study variation. Penalized quasi-likelihood (PQL), an approximate inference technique for generalized linear mixed models, and a linear model fitted by weighted ...
R W, Platt, B G, Leroux, N, Breslow
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Linear Equality Constraints in the General Linear Mixed Model

Biometrics, 2001
Scientists may wish to analyze correlated outcome data with constraints among the responses. For example, piecewise linear regression in a longitudinal data analysis can require use of a general linear mixed model combined with linear parameter constraints.
Edwards, Lloyd J., Stewart, Paul W., Muller, Keith E., Helms, Ronald W.   +3 more
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Generalized Linear Mixed Models

2017
For analyzing repeated measures data, the necessity of considering the relationships between outcome variables as well as between outcome variables and explanatory variable are of concern. We have discussed about such models in previous chapters. All the models proposed in various chapters are fixed effect models. However, in some cases, the dependence
M. Ataharul Islam, Rafiqul I. Chowdhury
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