Bayes variable selection in semiparametric linear models.

Published

Journal Article

There is a rich literature on Bayesian variable selection for parametric models. Our focus is on generalizing methods and asymptotic theory established for mixtures of g-priors to semiparametric linear regression models having unknown residual densities. Using a Dirichlet process location mixture for the residual density, we propose a semiparametric g-prior which incorporates an unknown matrix of cluster allocation indicators. For this class of priors, posterior computation can proceed via a straightforward stochastic search variable selection algorithm. In addition, Bayes factor and variable selection consistency is shown to result under a class of proper priors on g even when the number of candidate predictors p is allowed to increase much faster than sample size n, while making sparsity assumptions on the true model size.

Full Text

Duke Authors

Cited Authors

  • Kundu, S; Dunson, DB

Published Date

  • March 2014

Published In

Volume / Issue

  • 109 / 505

Start / End Page

  • 437 - 447

PubMed ID

  • 25071298

Pubmed Central ID

  • 25071298

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.2014.881153

Language

  • eng