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

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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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※ 編輯: wanting0605     來自: 140.113.252.176      (03/23 18:21)