Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37065
標題: Bayesian analysis of hierarchical linear mixed modeling using the multivariate t distribution
作者: Lin, T.I.
林宗儀
Lee, J.C.
關鍵字: autoregressive process
Bayesian prediction
Markov chain Monte Carlo
missing values
random effects
t linear mixed models
longitudinal data
covariance-structures
growth-curves
inference
time
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.
URI: http://hdl.handle.net/11455/37065
ISSN: 0378-3758
文章連結: http://dx.doi.org/10.1016/j.jspi.2005.12.010
Appears in Collections:統計學研究所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.