Johnson, Valen E. and Rossell, David (2012) Bayesian model selection in high-dimensional settings. Journal of the American Statistical Association, Vol.107 (No.498). pp. 649-660. doi:10.1080/01621459.2012.682536

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Abstract

Standard assumptions incorporated into Bayesian model selection procedures result in procedures that are not competitive with commonly used penalized likelihood methods. We propose modifications of these methods by imposing nonlocal prior densities on model parameters. We show that the resulting model selection procedures are consistent in linear model settings when the number of possible covariates p is bounded by the number of observations n, a property that has not been extended to other model selection procedures. In addition to consistently identifying the true model, the proposed procedures provide accurate estimates of the posterior probability that each identified model is correct. Through simulation studies, we demonstrate that these model selection procedures perform as well or better than commonly used penalized likelihood methods in a range of simulation settings. Proofs of the primary theorems are provided in the Supplementary Material that is available online.

Item Type:	Journal Article
Divisions:	Faculty of Science > Statistics
Journal or Publication Title:	Journal of the American Statistical Association
Publisher:	American Statistical Association
ISSN:	0162-1459
Official Date:	July 2012
Dates:	Date Event July 2012 Published
Volume:	Vol.107
Number:	No.498
Page Range:	pp. 649-660
DOI:	10.1080/01621459.2012.682536
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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