Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37067
標題: Bayesian inference in joint modelling of location and scale parameters of the t distribution for longitudinal data
作者: Lin, T.I.
林宗儀
Wang, W.L.
期刊/報告no:: Journal of Statistical Planning and Inference, Volume 141, Issue 4, Page(s) 1543-1553.
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/11455/37067
ISSN: 0378-3758
文章連結: http://dx.doi.org/10.1016/j.jspi.2010.11.001
Appears in Collections:統計學研究所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.