A strong uniform convergence rate of a kernel conditional quantile estimator under random left-truncation and dependent data

被引:5
作者
Ould-Said, Elias [1 ]
Yahia, Djabrane [2 ]
Necir, Abdelhakim [2 ]
机构
[1] Univ Littoral Cote dOpale, Univ Lille Nord France, LMPAJ Liouville, F-62228 Calais, France
[2] Univ Mohamed Khider, Lab Appl Math, Biskra 07000, Algeria
关键词
Kernel estimator; quantile function; late of convergence; strong mixing; strong uniform consistency; truncated data; PRODUCT-LIMIT ESTIMATOR; NONPARAMETRIC-ESTIMATION; ASYMPTOTIC PROPERTIES; TIME-SERIES; REPRESENTATIONS; PROBABILITY; DENSITY;
D O I
10.1214/08-EJS306
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 some asymptotic properties of the kernel conditional quantile estimator with randomly left-truncated data which exhibit some kind of dependence. We extend the result obtained by Lemdani, Quid-Said and Poulin [16] in the iid case. The uniform strong convergence rate of the estimator under strong mixing hypothesis is obtained,
引用
收藏
页码:426 / 445
页数:20
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