請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/36321
標題: Bayesian inference in joint modelling of location and scale parameters of the t distribution for longitudinal data
作者: Lin, Tsung-I
Wang, Wan-Lun
關鍵字: Cholesky decomposition
Data augmentation
Deviance information
criterion
Maximum likelihood estimation
Outliers
Predictive
distribution
maximum-likelihood-estimation
linear mixed models
posterior
distributions
covariance-matrix
markov-chains
performance
ec
摘要: This paper presents a fully Bayesian approach to multivariate t regression models whose mean vector and scale covariance matrix are modelled jointly for analyzing longitudinal data. The scale covariance structure is factorized in terms of unconstrained autoregressive and scale innovation parameters through a modified Cholesky decomposition. A computationally flexible data augmentation sampler coupled with the Metropolis-within-Gibbs scheme is developed for computing the posterior distributions of parameters. The Bayesian predictive inference for the future response vector is also investigated. The proposed methodologies are illustrated through a real example from a sleep dose-response study. (C) 2010 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/11455/36321
ISSN: 0378-3758
顯示於類別:應用數學系所

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