Open Access
Translator Disclaimer
December, 1984 Bandwidth Choice for Nonparametric Regression
John Rice
Ann. Statist. 12(4): 1215-1230 (December, 1984). DOI: 10.1214/aos/1176346788

Abstract

This paper is concerned with the problem of choosing a bandwidth parameter for nonparametric regression. We analyze a tapered Fourier series estimate and discuss the relationship of this estimate to a kernel estimate. We first consider a method based on an unbiased estimate of mean square error, and show that the bandwidth thus chosen is asymptotically optimal. Other methods are examined as well and are shown to be asymptotically equivalent. A small simulation shows, however, that for small or moderate sample size, the methods perform quite differently.

Citation

Download Citation

John Rice. "Bandwidth Choice for Nonparametric Regression." Ann. Statist. 12 (4) 1215 - 1230, December, 1984. https://doi.org/10.1214/aos/1176346788

Information

Published: December, 1984
First available in Project Euclid: 12 April 2007

zbMATH: 0554.62035
MathSciNet: MR760684
Digital Object Identifier: 10.1214/aos/1176346788

Subjects:
Primary: 62G99
Secondary: 62J99

Keywords: cross-validation , kernel regression , Nonparametric regression , smoothing

Rights: Copyright © 1984 Institute of Mathematical Statistics

JOURNAL ARTICLE
16 PAGES


SHARE
Vol.12 • No. 4 • December, 1984
Back to Top