On the rate of asymptotic normality of integral weighted kernel estimator in a non parametric regression model for φ -mixing random variables

Abstract

In this article, we study the convergence rate of asymptotic normality for the estimator in a non parametric regression model under φ-mixing random variables by using the blockwise technique. With different choices of the parameters, the rates are shown as O(n−1/9) and O(n−1/6). We also carry out some simulation studies and a real data analysis to support the theoretical results established here.