Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models

Random effects in generalized linear mixed models (GLMM) are used to explain the serial correlation of the longitudinal categorical data. Because the covariance matrix is high dimensional and should be positive definite, its structure is assumed to be constant over subjects and to be restricted such...

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Published inComputational statistics & data analysis Vol. 80; pp. 111 - 116
Main Authors Lee, Keunbaik, Yoo, Jae Keun
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2014
Subjects
autocorrelation
Bayesian analysis
Bayesian theory
Cholesky decomposition
Constants
Covariance matrix
Data processing
Heterogeneity
Longitudinal data
lung neoplasms
Mathematical models
Regression
regression analysis
Serials
Statistics
variance covariance matrix
Cholesky decomposition
Heterogeneity
Longitudinal data
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ISSN0167-9473
1872-7352
DOI10.1016/j.csda.2014.06.016

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Summary:Random effects in generalized linear mixed models (GLMM) are used to explain the serial correlation of the longitudinal categorical data. Because the covariance matrix is high dimensional and should be positive definite, its structure is assumed to be constant over subjects and to be restricted such as AR(1) structure. However, these assumptions are too strong and can result in biased estimates of the fixed effects. In this paper we propose a Bayesian modeling for the GLMM with regression models for parameters of the random effects covariance matrix using a moving average Cholesky decomposition which factors the covariance matrix into moving average (MA) parameters and IVs. We analyze lung cancer data using our proposed model.
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2014.06.016