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Model averaging estimation of generalized linear models with imputed covariates

  • Autori: Dardanoni, V; De Luca, G; Modica, S; Peracchi, F
  • Anno di pubblicazione: 2015
  • Tipologia: Articolo in rivista (Articolo in rivista)
  • OA Link:


We address the problem of estimating generalized linear models when some covariate values are missing 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 block model averaging strategy that incorporates information on the missing-data patterns and is computationally simple. An empirical application illustrates our approach.