標題: | Bayesian analysis of hierarchical linear mixed modeling using the multivariate t distribution |
作者: | Lin, Tsung I. Lee, Jack C. 統計學研究所 資訊管理與財務金融系 註:原資管所+財金所 Institute of Statistics Department of Information Management and Finance |
關鍵字: | autoregressive process;Bayesian prediction;Markov chain Monte Carlo;missing values;random effects;t linear mixed models |
公開日期: | 1-二月-2007 |
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://dx.doi.org/10.1016/j.jspi.2005.12.010 http://hdl.handle.net/11536/11186 |
ISSN: | 0378-3758 |
DOI: | 10.1016/j.jspi.2005.12.010 |
期刊: | JOURNAL OF STATISTICAL PLANNING AND INFERENCE |
Volume: | 137 |
Issue: | 2 |
起始頁: | 484 |
結束頁: | 495 |
顯示於類別: | 期刊論文 |