Title: A new nonparametric estimator of the regression function
Authors: Issam El Hattab - ENCG-Casablanca, Hassan 2 University (Morocco) [presenting]
Abstract: A new kernel-type estimator of the regression function is considered. The proposed methodology is based on expressing the regression function in terms of a copula density function. There are basically no restrictions on the choice of the kernel function in our setup, apart from satisfying some mild conditions. The selection of the bandwidth, however, is more problematic. Under some regularity conditions, we establish the asymptotic properties for the proposed estimator, namely uniform-in-bandwidth consistency with exact rate. Some numerical studies using Monte Carlo simulations and an empirical application are given to examine the finite-sample performance of our methods.