Bayesian variable selection via particle stochastic search

Minghui Shi and David B. Dunson

Statistics & Probability Letters, 2011, vol. 81, issue 2, 283-291

Abstract: We focus on Bayesian variable selection in regression models. One challenge is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In this article, we propose a new computational approach based on sequential Monte Carlo (SMC), which we refer to as particle stochastic search (PSS). We illustrate PSS through applications to linear regression and probit models.

Keywords: Bayes; factor; Marginal; inclusion; probability; Model; averaging; Model; uncertainty; Sequential; Monte; Carlo (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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