Extended Glivenko-Cantelli Theorem in Nonparametric Regression

被引：2
|
作者：
Cheng, Fuxia [1 ]
Yan, Jigao [2 ]
Yang, Lijian [3 ,4 ]
机构：
[1] Illinois State Univ, Dept Math, Normal, IL 61761 USA
[2] Soochow Univ, Sch Math Sci, Suzhou 215006, Peoples R China
[3] Ctr Adv Stat & Econometr Res, Suzhou, Peoples R China
[4] Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2014年 / 43卷 / 17期
关键词：
Empirical process; Glivenko-Cantelli theorem; Nonparametric regression; Residuals; DISTRIBUTION FUNCTION ESTIMATORS; STRONG UNIFORM CONSISTENCY; ERROR DENSITY;
D O I：
10.1080/03610926.2012.700377
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 consider the uniform strong consistency of the cumulative distribution function estimator in nonparametric regression. We obtain the extended Glivenko-Cantelli theorem for the residual-based empirical distribution function.
引用
收藏
页码：3720 / 3725
页数：6
相关论文
共 50 条
12345