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

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  • Tsai-Hung Fan
  • Yi-Fu Wang
  • Yi-Chen Zhang

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.

Suggested Citation

  • Tsai-Hung Fan & Yi-Fu Wang & Yi-Chen Zhang, 2014. "Bayesian model selection in linear mixed effects models with autoregressive(p) errors using mixture priors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1814-1829, August.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1814-1829
    DOI: 10.1080/02664763.2014.894001
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