Quasi-likelihood regression models with missing covariates

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Abstract
This paper presents methods to handle missing covariates when the quasi-likelihood equations for the complete data are available. Our suggestion is to replace the functions of the missing data appearing in the quasi-likelihood equation with their conditional means given the observed data or with unbiased predictors, so that the resulting equation is unbiased. We focus on two models. One is a random effects model for count data, where random effects are treated as missing covariates. The other is the overdispersed binomial regression model with partially missing covariates. We also investigate the efficiency of the proposed estimates relative to the maximum likelihood estimators.

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