The Annals of Statistics

Bandwidth Choice for Nonparametric Regression

John Rice

Full-text: Open access

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.

Article information

Source
Ann. Statist. Volume 12, Number 4 (1984), 1215-1230.

Dates
First available: 12 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.aos/1176346788

JSTOR
links.jstor.org

Digital Object Identifier
doi:10.1214/aos/1176346788

Mathematical Reviews number (MathSciNet)
MR760684

Zentralblatt MATH identifier
0554.62035

Subjects
Primary: 62G99: None of the above, but in this section
Secondary: 62J99: None of the above, but in this section

Keywords
Nonparametric regression kernel regression cross-validation smoothing

Citation

Rice, John. Bandwidth Choice for Nonparametric Regression. The Annals of Statistics 12 (1984), no. 4, 1215--1230. doi:10.1214/aos/1176346788. http://projecteuclid.org/euclid.aos/1176346788.


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