Abstract
In this paper, we consider kernel-based estimators in the nonparametric binary regression problem with multidimensional covariates. We propose a local linear type estimator of the response probability function with kernel weighted at each observed covariate. In addition, we discuss the rule of thumb bandwidth selector and the plug-in bandwidth selector. The efficiency of the weighted local linear estimator is determined from results of asymptotic properties and our simulation study.
