Robust nonparametric estimation for spatial regression

被引：14

作者：

Gheriballah, Abdelkader ^{[2
]}

Laksaci, Ali ^{[1
]}

Rouane, Rachida ^{[3
]}

机构：

[1] Univ Djillali Liabes Sidi Bel Abbes, Math Lab, Sidi Bel Abbes 22000, Algeria

[2] Univ Djillali Liabes Sidi Bel Abbes, Sidi Bel Abbes, Algeria

[3] Univ Moulay Taher, Saida, Algeria

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2010年 / 140卷 / 07期

关键词：

Asymptotic distribution; Almost complete convergence; Random field; Nonparametric regression; Kernel estimate; Bandwidth; Robust estimation; KERNEL DENSITY-ESTIMATION; RANDOM-FIELDS;

D O I：

10.1016/j.jspi.2010.01.042

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：

In this paper, we investigate a nonparametric robust estimation for spatial regression. More precisely, given a strictly stationary random field Z(i) = (X-i, Y-i)(i is an element of NN) (N >= 1), we consider a family of robust nonparametric estimators for a regression function based on the kernel method. Under some general mixing assumptions, the almost complete consistency and the asymptotic normality of these estimators are obtained. A robust procedure to select the smoothing parameter adapted to the spatial data is also discussed. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1656 / 1670

页数：15

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