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

Jia Chiun Pan, Mei Hsien Lee, Miao-Yu Tsai

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)2234-2247
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Volume47
Issue number8
DOIs
Publication statusPublished - 2018 Sep 14

Fingerprint

Bayesian Variable Selection
Reversible Jump Markov Chain Monte Carlo
Markov Chain Monte Carlo Algorithms
Logistic Model
Mixed Model
Markov processes
Logistics
Markov Chain Monte Carlo Methods
Monte Carlo methods
Simulation Study
Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation

Cite this

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Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models. / Pan, Jia Chiun; Lee, Mei Hsien; Tsai, Miao-Yu.

In: Communications in Statistics: Simulation and Computation, Vol. 47, No. 8, 14.09.2018, p. 2234-2247.

Research output: Contribution to journalArticle

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