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

In this article, we study the convergence rate of asymptotic normality for the estimator in a non parametric regression model under phi-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.