Consistent high-dimensional Bayesian variable selection via penalized credible regions.

ABSTRACT: For high-dimensional data, particularly when the number of predictors greatly exceeds the sample size, selection of relevant predictors for regression is a challenging problem. Methods such as sure screening, forward selection, or penalized regressions are commonly used. Bayesian variable selection methods place prior distributions on the parameters along with a prior over model space, or equivalently, a mixture prior on the parameters having mass at zero. Since exhaustive enumeration is not feasible, posterior model probabilities are often obtained via long MCMC runs. The chosen model can depend heavily on various choices for priors and also posterior thresholds. Alternatively, we propose a conjugate prior only on the full model parameters and use sparse solutions within posterior credibl

SUBMITTER: Bondell HD 

PROVIDER: S-EPMC3587767 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature