Convergence rate of the kernel regression estimator for associated and truncated data

被引：1

作者：

Guessoum, Z. ^{[1
]}

Hamrani, F. ^{[2
]}

机构：

[1] USTHB, Fac Math, Lab MSTD, BP 32, Algiers 16111, Algeria

[2] USTHB, Fac Math, Algiers, Algeria

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2017年 / 29卷 / 02期

关键词：

Association; kernel estimator; nonparametric regression; random left-truncation (RLT) model; rate of convergence; strong consistency; PRODUCT-LIMIT ESTIMATOR; LYNDEN-BELL ESTIMATOR; ASYMPTOTIC PROPERTIES; DENSITY-ESTIMATION; RANDOM-VARIABLES; PROBABILITY; NORMALITY;

D O I：

10.1080/10485252.2017.1303059

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：

This paper studies the behaviour of the kernel estimator of the regression function for associated data in the random left truncated model. The uniform strong consistency rate over a real compact set of the estimate is established. The finite sample performance of the estimator is investigated through extensive simulation studies.

引用

页码：425 / 446

页数：22

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