The Annals of Statistics

Model selection in nonparametric regression

Marten Wegkamp

Full-text: Open access

Abstract

Model selection using a penalized data-splitting device is studied in the context of nonparametric regression. Finite sample bounds under mild conditions are obtained. The resulting estimates are adaptive for large classes of functions.

Article information

Source
Ann. Statist., Volume 31, Number 1 (2003), 252-273.

Dates
First available in Project Euclid: 26 February 2003

Permanent link to this document
https://projecteuclid.org/euclid.aos/1046294464

Digital Object Identifier
doi:10.1214/aos/1046294464

Mathematical Reviews number (MathSciNet)
MR1962506

Zentralblatt MATH identifier
1019.62037

Subjects
Primary: 60F05: Central limit and other weak theorems 60F17: Functional limit theorems; invariance principles
Secondary: 60G15: Gaussian processes 62E20: Asymptotic distribution theory

Keywords
Adaptive estimation classification data-splitting least squares estimation model selection penalized least squares VC-major classes

Citation

Wegkamp, Marten. Model selection in nonparametric regression. Ann. Statist. 31 (2003), no. 1, 252--273. doi:10.1214/aos/1046294464. https://projecteuclid.org/euclid.aos/1046294464


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  • NEW HAVEN, CONNECTICUT 06520 E-MAIL: marten.wegkamp@yale.edu