Two level stochastic search variable selection in GLMs with missing predictors

Mitra, Robin ORCID: https://orcid.org/0000-0001-9584-8044 and Dunson, David D. 2009. Two level stochastic search variable selection in GLMs with missing predictors. [Project Report]. Southampton: University of Southampton. Available at: https://eprints.soton.ac.uk/65460/1/65460-01.pdf

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

Item Type:	Monograph (Project Report)
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Publisher:	University of Southampton
Last Modified:	06 May 2023 02:00
URI:	https://orca.cardiff.ac.uk/id/eprint/146468

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