The University of Southampton
University of Southampton Institutional Repository

Two-level stochastic search variable selection in GLMs with missing predictors

Two-level stochastic search variable selection in GLMs with missing predictors
Two-level stochastic search variable selection in GLMs with missing predictors
Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

1557-4679
33
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Dunson, David
560f17c5-3851-4163-a8e4-d3b12ad9650c
Mitra, Robin
2b944cd7-5be8-4dd1-ab44-f8ada9a33405
Dunson, David
560f17c5-3851-4163-a8e4-d3b12ad9650c

Mitra, Robin and Dunson, David (2010) Two-level stochastic search variable selection in GLMs with missing predictors. International Journal of Biostatistics, 6 (1), 33. (doi:10.2202/1557-4679.1173).

Record type: Article

Abstract

Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

This record has no associated files available for download.

More information

Published date: January 2010
Organisations: Statistics

Identifiers

Local EPrints ID: 181607
URI: http://eprints.soton.ac.uk/id/eprint/181607
ISSN: 1557-4679
PURE UUID: 0ae93c11-d821-4232-b629-0cf0e1918c13

Catalogue record

Date deposited: 19 Apr 2011 10:34
Last modified: 14 Mar 2024 02:56

Export record

Altmetrics

Contributors

Author: Robin Mitra
Author: David Dunson

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×