Statistics Papers

Document Type

Journal Article

Date of this Version

2008

Publication Source

The Annals of Statistics

Volume

36

Issue

2

Start Page

646

Last Page

664

DOI

10.1214/009053607000000901

Abstract

Variance function estimation in nonparametric regression is considered and the minimax rate of convergence is derived. We are particularly interested in the effect of the unknown mean on the estimation of the variance function. Our results indicate that, contrary to the common practice, it is not desirable to base the estimator of the variance function on the residuals from an optimal estimator of the mean when the mean function is not smooth. Instead it is more desirable to use estimators of the mean with minimal bias. On the other hand, when the mean function is very smooth, our numerical results show that the residual-based method performs better, but not substantial better than the first-order-difference-based estimator. In addition our asymptotic results also correct the optimal rate claimed in Hall and Carroll [J. Roy. Statist. Soc. Ser. B 51 (1989) 3–14].

Keywords

minimax estimation, nonparametric regression, variance estimation

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Date Posted: 27 November 2017

This document has been peer reviewed.