Statistics Papers

Document Type

Journal Article

Date of this Version

2008

Publication Source

The Annals of Statistics

Volume

36

Issue

5

Start Page

2055

Last Page

2084

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.

Keywords

adaptivity, asymptotic equivalence, James–Stein estimator, moderate large deviation, nonparametric regression, quantile coupling, robust estimation wavelets, robust estimation, wavelets

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Date Posted: 27 November 2017

This document has been peer reviewed.