On nonparametric estimation of the regression function under random censorship model

被引：28

作者：

Guessoum, Zohra ^{[1
]}

Ould-Said, Elias ^{[2
]}

机构：

[1] Univ Sci & Tech HB, Fac Math, El Alia 16111, Algeria

[2] Univ Littoral Cote dOpale, LMPAJ Liouville, F-62228 Calais, France

来源：

STATISTICS & RISK MODELING | 2008年 / 26卷 / 03期

关键词：

Asymptotic normality; censored data; Kaplan-Meier estimator; kernel; nonparametric regression; rate of convergence; strong consistency;

D O I：

10.1524/stnd.2008.0919

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 study the behavior of a kernel estimator for the regression function in a random right-censoring model. We establish pointwise and uniform strong consistency over a compact set and give a rate of convergence for the estimate. The asymptotic normality of the estimate is also proved. Simulations are drawn for different cases to illustrate both, convergence and asymptotic normality.

引用

页码：159 / 177

页数：19

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