Asymptotically efficient estimates for nonparametric regression models

被引：14

作者：

Galtchouk, L

Pergamenshchikov, S

机构：

[1] Univ Strasbourg, Dept Math, IRMA, F-67084 Strasbourg, France

[2] Univ Rouen, UFR Sci, Lab Math Raphael Salem, CNRS,UMR 6085, F-76821 St Etienne, France

来源：

STATISTICS & PROBABILITY LETTERS | 2006年 / 76卷 / 08期

关键词：

asymptotical efficiency; kernel estimates; minimax; nonparametric regression;

D O I：

10.1016/j.spl.2005.10.017

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The paper deals with estimating problem of regression function at a given state point in nonparametric regression models with Gaussian noises and with non-Gaussian noises having unknown distribution. An asymptotically efficient kernel estimator is constructed for a minimax risk. (C) 2005 Elsevier B.V. All rights reserved.

引用

页码：852 / 860

页数：9

共 18 条

[1] Risk bounds for model selection via penalization [J].