Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

ABSTRACT

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.

KEYWORDS:

• Bayesian variable selection
• Holmes and Held algorithm
• Logistic mixed model
• Reversible jump Markov chain Monte Carlo
• Stochastic search variable selection