Statistics and Data Science Seminar
Yinxiao Huang
University of Chicago
Kernel density estimation for time series: an asymptotic theory
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.
Wednesday December 1, 2010 at 3:00 PM in SEO 636