On the Nadaraya-Watson kernel regression estimator for irregularly spaced spatial data

被引：5

作者：

El Machkouri, Mohamed ^{[1
]}

Fan, Xiequan ^{[2
]}

Reding, Lucas ^{[1
]}

机构：

[1] Univ Rouen Normandie, Lab Math Raphael Salem, UMR CNRS 6085, Rouen, France

[2] Tianjin Univ, Ctr Appl Math, Tianjin, Peoples R China

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2020年 / 205卷

基金：

中国国家自然科学基金;

关键词：

Nadaraya-Watson estimator; Strong mixing; Random fields; Asymptotic normality; Physical dependence measure; CENTRAL-LIMIT-THEOREM; INEQUALITY; MODEL;

D O I：

10.1016/j.jspi.2019.06.006

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 investigate the asymptotic normality of the Nadaraya-Watson kernel regression estimator for irregularly spaced data collected on a finite region of the lattice Z(d) where d is a positive integer. The results are stated for strongly mixing random fields in the sense of Rosenblatt (1956) and for weakly dependent random fields in the sense of Wu (2005). Only minimal conditions on the bandwidth parameter and simple conditions on the dependence structure of the data are assumed. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：92 / 114

页数：23

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