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

Jia Chiun Pan, Mei Hsien Lee, Miao Yu Tsai

研究成果: Article

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)2234-2247
頁數14
期刊Communications in Statistics: Simulation and Computation
47
發行號8
DOIs
出版狀態Published - 2018 九月 14

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation

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