Yinxiao Huang : Kernel density estimation for time series: an asymptotic theory

Posted by , part of the Statistics and Data Science Seminar.

At

Dec. 1, 2010, 3 p.m.

In

SEO 636

Abstract

In this paper, we give a unified analysis of both the nonparametric kernel density estimator and regression estimator under our dependence structure. Asymptotic results such as uniform convergence rate, $L^p$ convergence rate, asymptotic normality are obtained under fairly mild conditions. In particular, we allow certain long memory processes as well. A closely related problem, the recursive kernel estimator where the bandwidth changes with each observation, is still in progress and I hope to get it done pretty soon. Therefore, I may talk about the recursive estimator as well if time permits.