Bayesian inference in joint modelling of location and scale parameters of the t distribution for longitudinal data

Tsung I. Lin, Wan Lun Wang

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1543-1553
Number of pages11
JournalJournal of Statistical Planning and Inference
Volume141
Issue number4
DOIs
Publication statusPublished - 2011 Apr

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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