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Shen-Ming Lee

2012-03-16
13:00:00 - 14:40:00

103 , Mathematics Research Center Building (ori. New Math. Bldg.)

We propose estimation methods for fitting logistic regression with outcome and covariate variables missing separately or simultaneously. One of the two proposed estimators is an extension of the validation likelihood estimator of Breslow and Cain (1988). The other is a joint conditional likelihood estimator based on both validation and non-validation data. Large sample properties of the proposed estimators are studied under certain regularity conditions. Simulation results show that the joint conditional likelihood estimator is more efficient than the validation likelihood estimator, weighted estimator, and complete-case estimator. The practical use of the proposed methods is illustrated with data from a cable TV study in Taiwan.