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October 2008 Robust nonparametric estimation via wavelet median regression
Lawrence D. Brown, T. Tony Cai, Harrison H. Zhou
Ann. Statist. 36(5): 2055-2084 (October 2008). DOI: 10.1214/07-AOS513

Abstract

In this paper we develop a nonparametric regression method that is simultaneously adaptive over a wide range of function classes for the regression function and robust over a large collection of error distributions, including those that are heavy-tailed, and may not even possess variances or means. Our approach is to first use local medians to turn the problem of nonparametric regression with unknown noise distribution into a standard Gaussian regression problem and then apply a wavelet block thresholding procedure to construct an estimator of the regression function. It is shown that the estimator simultaneously attains the optimal rate of convergence over a wide range of the Besov classes, without prior knowledge of the smoothness of the underlying functions or prior knowledge of the error distribution. The estimator also automatically adapts to the local smoothness of the underlying function, and attains the local adaptive minimax rate for estimating functions at a point.

A key technical result in our development is a quantile coupling theorem which gives a tight bound for the quantile coupling between the sample medians and a normal variable. This median coupling inequality may be of independent interest.

Citation

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Lawrence D. Brown. T. Tony Cai. Harrison H. Zhou. "Robust nonparametric estimation via wavelet median regression." Ann. Statist. 36 (5) 2055 - 2084, October 2008. https://doi.org/10.1214/07-AOS513

Information

Published: October 2008
First available in Project Euclid: 13 October 2008

zbMATH: 1148.62019
MathSciNet: MR2458179
Digital Object Identifier: 10.1214/07-AOS513

Subjects:
Primary: 62G08
Secondary: 62G20

Keywords: Adaptivity , ‎asymptotic ‎equivalence , James–Stein estimator , moderate large deviation , Nonparametric regression , Quantile coupling , robust estimation , Wavelets

Rights: Copyright © 2008 Institute of Mathematical Statistics

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Vol.36 • No. 5 • October 2008
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