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Two level stochastic search variable selection in GLMs with missing predictors

Record type: Monograph (Working Paper)

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 this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

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Citation

Mitra, Robin and Dunson, David D. (2009) Two level stochastic search variable selection in GLMs with missing predictors , Southampton, UK Southampton Statistical Sciences Research Institute 28pp. (S3RI Methodology Working Papers, M09/04).

More information

Published date: 17 February 2009
Keywords: missing at random, model averaging, multiple imputation, stochastic search, subset selection, variable selection

Identifiers

Local EPrints ID: 65460
URI: http://eprints.soton.ac.uk/id/eprint/65460
PURE UUID: b911a37a-e89b-4fd0-9f6c-2cd3bae4cd98

Catalogue record

Date deposited: 17 Feb 2009
Last modified: 19 Jul 2017 00:34

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Contributors

Author: Robin Mitra
Author: David D. Dunson

University divisions

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