題　目：Bayesian Inference in Multivariate t Linear Mixed Models using the IBF-Gibbs Sampler 主講人：王婉倫教授(逢甲大學統計系) 時　間：100年3月25日(星期五)上午10:40-11:30 (上午10:20-10:40茶會於交大統計所429室舉行) 地　點：交大綜合一館427室 -- Abstract The multivariate linear mixed model (MLMM) has become the most widely used tool for analyzing multi-outcome longitudinal data. Motivated by a concern of sensitivity to potential outliers or data with longer-than-normal tails, we develop a robust extension of the MLMM that is constructed by using the multivariate t distribution, called the multivariate t linear mixed model (MtLMM). In addition, an AR(p) structure is specified as a parsimonious way of taking into account the dependency of observations. In the talk, I will present a fully Bayesian approach to the MtLMM. 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 formulae (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 predictive inferences for future values are also investigated. The proposed methodologies are illustrated with a real example from an AIDS clinical trial and a careful designed simulation study. --

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