Uniform convergence rate of kernel estimation with mixed categorical and continuous data

Abstract

We consider nonparametric estimation of regression functions with mixed categorical and continuous data, and we smooth both the discrete and continuous variables so that the approach does not suffer the sample splitting problem associated with the conventional frequency-based nonparametric estimation method. We derive the uniform convergence rate of the kernel regression function estimation with weakly dependent data.