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Biometrika 1996 83(4):825-834; doi:10.1093/biomet/83.4.825
© 1996 by Biometrika Trust
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Quasi-likelihood regression models with missing covariates

MYUNGHEE CHO PAIK

Division of Biostatistics, Columbia University 600 West 168th Street, New York City, New York 10032, U.S.A.

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.

Key Words: Efficiency • Estimating equation • Missing covariates • Quasi-likelihood • Random effects


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