This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 662
Type: Topic Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #308376
Title: Kernel Estimation of Time Series: An Asymptotic Theory
Author(s): Weibiao Wu+ and Yinxiao Huang* and Yibi Huang
Companies: The University of Chicago and The University of Chicago and The University of Chicago
Address: 5734 S. University Ave, Chicago, IL, 60637,
Keywords: Nonlinear time series ; Kernel estimation ; Regression ; Prediction Theory
Abstract:

The paper considers kernel density and regression estimation problems for a wide class of nonlinear time series models. Asymptotic normality and uniform convergence rates of kernel estimators are established under mild regularity conditions. Our theory is developed under the new framework of predictive dependence measures which are directly related to the data-generating mechanisms of the underlying processes. The imposed conditions are different from the classical strong mixing conditions and they are related to the sensitivity measure in the prediction theory of nonlinear time series.


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