The Annals of Statistics

Optimal Bandwidth Selection in Nonparametric Regression Function Estimation

Wolfgang Hardle and James Stephen Marron

Full-text: Open access

Abstract

Kernel estimators of an unknown multivariate regression function are investigated. A bandwidth-selection rule is considered, which can be formulated in terms of cross validation. Under mild assumptions on the kernel and the unknown regression function, it is seen that this rule is asymptotically optimal.

Article information

Source
Ann. Statist., Volume 13, Number 4 (1985), 1465-1481.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349748

Digital Object Identifier
doi:10.1214/aos/1176349748

Mathematical Reviews number (MathSciNet)
MR811503

Zentralblatt MATH identifier
0594.62043

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62G20: Asymptotic properties

Keywords
Nonparametric regression estimation kernel estimators optimal bandwidth smoothing parameter cross validation

Citation

Hardle, Wolfgang; Marron, James Stephen. Optimal Bandwidth Selection in Nonparametric Regression Function Estimation. Ann. Statist. 13 (1985), no. 4, 1465--1481. doi:10.1214/aos/1176349748. https://projecteuclid.org/euclid.aos/1176349748


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