Model averaging estimation of generalized linear models with imputed covariates

We address the problem of estimating generalized linear models when some covariate values aremissing but imputations are available to fill-in the missing values. This situation generates a bias-precision tradeoff in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a problem of model uncertainty using Bayesian averaging of classical maximum likelihood estimators (BAML). We also propose a blockmodel averaging strategy that incorporates information on themissing-data patterns and is computationally simple. An empirical application illustrates our approach. © 2014 Elsevier B.V. All rights reserved.