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

Keunbaik Lee, Jae Keun Yoo

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)111-116
Number of pages6
JournalComputational Statistics and Data Analysis
Volume80
DOIs
StatePublished - Dec 2014

Keywords

  • Cholesky decomposition
  • Heterogeneity
  • Longitudinal data

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