Abstract: We consider the estimation problem of a logistic regression model. We assume the response observations and covariate values are both subject to measurement errors. We discuss some parametric and semiparametric estimation methods using mismeasured observations with validation data and derive their asypmtotic distributions. Our results are extentions of some well known results in the literature. Comparisons of the asymptotic covariance matrices of the studied estimators are made, and some lower and upper bounds for the asymptotic relative efficiencies are given to show the advantages of the semiparametric method. Some simulation results also show the method performs well.
Key words and phrases: Kernel estimation, estimated likelihood, logistic regression, measurement error, misclassification.