JSM 2003 Online Program

Abstract #301775

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Activity Number:	56
Type:	Contributed
Date/Time:	Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor:	Section on Nonparametric Statistics
Abstract - #301775
Title:	Nonparametric Regression with Infinite Order Flat-Top Kernels
Author(s):	Timothy L. McMurry*+
Companies:	University of California, San Diego
Address:	Department of Mathematics, La Jolla, CA, 92093-0112,
Keywords:	nonparametric regression ; bias reduction ; Fourier transform
Abstract:	The problem of nonparametric regression is addressed, and a kernel smoothing estimator is proposed which has favorable asymptotic performance (bias, variance, and mean squared error). The proposed class of kernels is characterized by a Fourier transform which is flat near the origin and infinitely differentiable. This property allows the bias of the estimate to decrease at the maximal rate without harming the rate at which the variance decreases, thus leading to a faster rate of convergence.

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