Two level stochastic search variable selection in GLMs with missing predictors

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).


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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 (Working Paper)
Keywords: missing at random, model averaging, multiple imputation, stochastic search, subset selection, variable selection
Subjects: H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 65460
Accepted Date and Publication Date:
17 February 2009Made publicly available
Date Deposited: 17 Feb 2009
Last Modified: 31 Mar 2016 12:50

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