Log-logistic parameters estimation using moving extremes ranked set sampling design

被引：11
|
作者：
He Xiao-fang [1 ]
Chen Wang-xue [1 ]
Yang Rui [1 ]
机构：
[1] Jishou Univ, Dept Math & Stat, Jishou 416000, Peoples R China
来源：
APPLIED MATHEMATICS-A JOURNAL OF CHINESE UNIVERSITIES SERIES B | 2021年 / 36卷 / 01期
基金：
中国国家自然科学基金;
关键词：
moving extremes ranked set sample; best linear unbiased estimator; maximum likelihood estimator;
D O I：
10.1007/s11766-021-3720-y
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In statistical parameter estimation problems, how well the parameters are estimated largely depends on the sampling design used. In the current paper, a modification of ranked set sampling (RSS) called moving extremes RSS (MERSS) is considered for the estimation of the scale and shape parameters for the log-logistic distribution. Several traditional estimators and ad hoc estimators will be studied under MERSS. The estimators under MERSS are compared to the corresponding ones under SRS. The simulation results show that the estimators under MERSS are significantly more efficient than the ones under SRS.
引用
收藏
页码：99 / 113
页数：15
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