摘要： The multivariate linear mixed model (MLMM) has become the most widely used tool for analyzing multi-outcome longitudinal data. Although it offers great flexibility for modeling the between- and within-subject correlation among multi-outcome repeated measures, the underlying normality assumption is vulnerable to potential atypical observations. We present a fully Bayesian approach to the multivariate t linear mixed model (MtLMM), which is a robust extension of MLMM with the random effects and errors jointly distributed as a multivariate t distribution. Owing to the introduction of too many hidden variables in the model, the conventional Markov chain Monte Carlo (MCMC) method may converge painfully slowly and thus fails to provide valid inference. To alleviate this problem, a computationally efficient inverse Bayes formulas (IBF) sampler coupled with the Gibbs scheme, called the IBF-Gibbs sampler, is developed and shown to be effective in drawing samples from the target distributions. The issues related to model determination and Bayesian predictive inference for future values are also investigated. The proposed methodologies are illustrated with a real example from an AIDS clinical trial and a careful simulation study. 出版者： New York: Elsevier Inc 出版日期： 2012-02-01 出處： Journal of multivariate analysis, 2012-02, Vol.105 (1), p.300-310 資源來源： RePEc 版權： 2011 Elsevier Inc. 版權： Copyright Taylor & Francis Group Feb 2012 識別號： ISSN: 0047-259X 識別號： EISSN: 1095-7243 識別號： DOI: 10.1016/j.jmva.2011.10.006 識別號： CODEN: JMVAAI
