Adaptive asymptotically efficient estimation in heteroscedastic nonparametric regression

被引：0

作者：

Leonid Galtchouk

Sergey Pergamenshchikov

机构：

[1] Louis Pasteur Strasbourg University,Department of Mathematics

[2] Avenue de l’Université,Laboratoire de Mathématiques Raphael Salem

[3] BP. 12,undefined

[4] Université de Rouen,undefined

来源：

Journal of the Korean Statistical Society | 2009年 / 38卷

关键词：

primary 62G08; secondary 62G05; 62G20; Asymptotic bounds; Adaptive estimation; Efficient estimation; Heteroscedastic regression; Nonparametric regression; Pinsker’s constant;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The paper considers some asymptotic properties of the adaptive procedure proposed in authors’ paper, 2007, for estimating an unknown nonparametric regression. We show that the procedure is asymptotically efficient for quadratic risk, i.e. the asymptotic quadratic risk of the procedure coincides with the corresponding Pinsker constant provided the sharp lower bound for the quadratic risk over all possible estimators.

引用

页码：305 / 322

页数：17

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