کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6869787 681379 2014 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 80, December 2014, Pages 111-116
نویسندگان
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