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- W2185489189 abstract "This article considers semiparametric estimation in logistic regression with missing covariates. In a validation subsample, we assume covariates are mea- sured without error. Some covariates are missing in the non-validation set, while surrogate variables may be available for all study subjects. We consider the case when a covariate variable is missing at random such that the selection probability of the validation set depends only on observed data. Breslow and Cain (1988) pro- posed a conditional likelihood approach based on the validation set. We combine the conditional likelihoods of the validation set and the non-validation set. The proposed estimator is easy to implement and is semiparametric since no additional model assumption is imposed. Large sample theory is developed. For the esti- mation of the parameter for the missing covariate, simulations show that, under various situations, the proposed estimator is significantly more efficient than the validation likelihood estimator of Breslow and Cain and the inverse selection prob- ability weighted estimator. Under moderate sample sizes and moderate values of relative risk parameters, our estimator remains competitive when compared with the nonparametric maximum likelihood estimator of Scott and Wild (1997). The proposed method is illustrated by a real data example." @default.
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- W2185489189 date "2002-01-01" @default.
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- W2185489189 title "JOINT CONDITIONAL LIKELIHOOD ESTIMATOR IN LOGISTIC REGRESSION WITH MISSING COVARIATE DATA" @default.
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