Nonparametric estimators for quantile density function under length-biased sampling

被引：6
|
作者：
Akbari, Mahboubeh [1 ]
Rezaei, Majid [1 ]
Jomhoori, Sarah [1 ]
Fakoor, Vahid [2 ]
机构：
[1] Univ Birjand, Dept Stat, Birjand 97175615, Iran
[2] Ferdowsi Univ Mashhad, Sch Math Sci, Dept Stat, Mashhad, Razavi Khorasan, Iran
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2019年 / 48卷 / 19期
关键词：
Asymptotic properties; convergence rate; gaussian process; length-biased sampling; quantile density function; DISTRIBUTIONS;
D O I：
10.1080/03610926.2018.1549245
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 article, the strong uniform consistency of two nonparametric estimators for the quantile density function is established under length-biased sampling. The rate of the strong approximation of the resulting processes of these estimators will be presented as well. A Monte Carlo simulation study is carried out to compare the proposed estimators with each other in terms of mean squared errors.
引用
收藏
页码：4918 / 4935
页数：18
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