Perfect sampling for Bayesian variable selection in a linear regression model

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• 作者：Schneider ; U. ; Corcoran ; J.N.
• 关键词：60J10 ; 65C05 ; 62F25 ; 60K30 ; Invariant measures ; Backwards coupling ; Coupling from the past ; Exact sampling ; Perfect sampling ; Slice coupling ; Shift coupling ; Bayesian variable selection
• 刊名：Journal of Statistical Planning and Inference
• 出版年：2004
• 出版时间：November 1, 2004
• 年：2004
• 卷：126
• 期：1
• 页码：153-171
• 全文大小：301 K

文摘

We describe the use of perfect sampling algorithms for Bayesian variable selection in a linear regression model. Starting with a basic case solved by Huang and Djuric (EURASIP J. Appl. Si. Pr. 1 (2002) 38), where the model coefficients and noise variance are assumed to be known, we generalize the model step by step to allow for other sources of randomness. We specify perfect simulation algorithms that solve these cases by incorporating various techniques including Gibbs sampling, the perfect independent Metropolis–Hastings (IMH) algorithm, and recently developed “slice coupling” algorithms. Applications to simulated data sets suggest that our algorithms perform well in identifying relevant predictor variables.