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標題: Bayesian analysis of hierarchical linear mixed modeling using the multivariate t distribution
作者: Lin, T.I.
Lee, J.C.
關鍵字: autoregressive process
Bayesian prediction
Markov chain Monte Carlo
missing values
random effects
t linear mixed models
longitudinal data
期刊/報告no:: Journal of Statistical Planning and Inference, Volume 137, Issue 2, Page(s) 484-495.
摘要: This article presents a fully Bayesian approach to modeling incomplete longitudinal data using the t linear mixed model with AR(p) dependence. Markov chain Monte Carlo (MCMC) techniques are implemented for computing posterior distributions of parameters. To facilitate the computation, two types of auxiliary indicator matrices are incorporated into the model. Meanwhile, the constraints on the parameter space arising from the stationarity conditions for the autoregressive parameters are handled by a reparametrization scheme. Bayesian predictive inferences for the future vector are also investigated. An application is illustrated through a real example from a multiple sclerosis clinical trial. (c) 2006 Elsevier B.V. All rights reserved.
ISSN: 0378-3758
Appears in Collections:統計學研究所



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