Asymptotic properties of a nonparametric regression function estimator with randomly truncated data

被引：50
|
作者：
Ould-Said, Elias
Lemdani, Mohamed
机构：
[1] Univ Lille 2, Lab Biomaths, EA 3614, Fac Pharm, F-59006 Lille, France
[2] Univ Littoral Cote dOpale, LMPA J Liouville, F-62228 Calais, France
来源：
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2006年 / 58卷 / 02期
关键词：
asymptotic normality; kernel; nonparametric regression; rate of convergence; strong consistency; truncated data; V-C class;
D O I：
10.1007/s10463-005-0011-y
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 define a new kernel estimator of the regression function under a left truncation model. We establish the pointwise and uniform strong consistency over a compact set and give a rate of convergence of the estimate. The pointwise asymptotic normality of the estimate is also given. Some simulations are given to show the asymptotic behavior of the estimate in different cases. The distribution function and the covariable's density are also estimated.
引用
收藏
页码：357 / 378
页数：22
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