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
收藏
页码：1656 / 1670
页数：15
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