Maximum Likelihood and Semiparametric Estimation in Logistic Models with Incomplete Covariate Data

Maximum likelihood estimation of regression parameters with
incomplete covariate information usually requires a distributional
assumption about the concerned covariates which implies a source of
misspecification. Semiparametric procedures avoid such assumptions
at the expense of efficiency. A simulation study is carried out to
get an idea of the performance of the maximum likelihood estimator
under misspecification and to compare the semiparametric procedures
with the maximum likelihood estimator when the latter is based on a
correct assumption.