Asymptotic normality of kernel estimates in a regression model for random fields

被引：13

作者：

El Machkouri, Mohamed ^{[1
]}

Stoica, Radu ^{[2
]}

机构：

[1] Univ Rouen, Lab Math Raphael Salem, UMR CNRS 6085, F-76801 St Etienne, France

[2] Univ Lille 1, Lab Math Paul Painleve, UMR CNRS 8524, UFR Math Pures & Appl, F-59655 Villeneuve Dascq, France

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2010年 / 22卷 / 08期

关键词：

nonparametric regression estimation; asymptotic normality; kernel estimator; strongly mixing random field; CENTRAL-LIMIT-THEOREM; DENSITY-ESTIMATION;

D O I：

10.1080/10485250903505893

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：

We establish the asymptotic normality of the regression estimator in a fixed-design setting when the errors are given by a field of dependent random variables. The result applies to martingale-difference or strongly mixing random fields. On this basis, a statistical test that can be applied to image analysis is also presented.

引用

页码：955 / 971

页数：17

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