Bayesian model selection in linear mixed effects models with autoregressive(p) errors using mixture priors

Tsai Hung Fan, Yi Fu Wang, Yi Chen Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this article, we apply the Bayesian approach to the linear mixed effect models with autoregressive(p) random errors under mixture priors obtained with the Markov chain Monte Carlo (MCMC) method. The mixture structure of a point mass and continuous distribution can help to select the variables in fixed and random effects models from the posterior sample generated using the MCMC method. Bayesian prediction of future observations is also one of the major concerns. To get the best model, we consider the commonly used highest posterior probability model and the median posterior probability model. As a result, both criteria tend to be needed to choose the best model from the entire simulation study. In terms of predictive accuracy, a real example confirms that the proposed method provides accurate results.

Original languageEnglish
Pages (from-to)1814-1829
Number of pages16
JournalJournal of Applied Statistics
Volume41
Issue number8
DOIs
StatePublished - Aug 2014

Keywords

  • autoregressive models
  • highest posterior probability model
  • linear mixed effects models
  • median probability model
  • mixture priors

Fingerprint

Dive into the research topics of 'Bayesian model selection in linear mixed effects models with autoregressive(p) errors using mixture priors'. Together they form a unique fingerprint.

Cite this