Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models
- 주제(키워드) Cholesky decomposition , Longitudinal data , Heterogeneity
- 등재 SCIE, SCOPUS
- 발행기관 ELSEVIER SCIENCE BV
- 발행년도 2014
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000108805
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.csda.2014.06.016
초록/요약
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 ARC 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. (C) 2014 Elsevier B.V. All rights reserved.
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