Uniform in bandwidth consistency of nonparametric regression based on copula representation

被引：13

作者：

Bouzebda, Salim ^{[1
]}

Elhattab, Issam ^{[2
]}

Seck, Cheikh Tidiane ^{[3
]}

机构：

[1] Univ Technol Compiegne, Sorbonne Univ, LMAC Lab Appl Math Compiegne, Compiegne, France

[2] Hassan II Univ Casablanca, ENCG, Casablanca, Morocco

[3] Univ Alioune Diop, EReSMA Equipe Rech Stat & Modeles Aleatoires, Bambey, Senegal

来源：

STATISTICS & PROBABILITY LETTERS | 2018年 / 137卷

关键词：

Dependence function; Uniform consistency; Copula representation; Kernel-type-estimator; Uniform in bandwidth; Empirical process; KERNEL DENSITY ESTIMATORS;

D O I：

10.1016/j.spl.2018.01.021

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 in this paper the uniform-in-bandwidth consistency for general kernel-type estimators of regression. More precisely, we investigate the regression estimator based on copula representation. The asymptotic properties are driven by a U-statistic relying on the methods developed in Dony and Mason (2008). (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：173 / 182

页数：10

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