Log-extended exponential-geometric parameters estimation using simple random sampling and moving extremes ranked set sampling

被引：7

作者：

Yang, Rui ^{[1
]}

Chen, Wangxue ^{[1
]}

Dong, Yanfei ^{[1
]}

机构：

[1] Jishou Univ, Dept Math & Stat, Jishou 416000, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2023年 / 52卷 / 01期

基金：

美国国家科学基金会;

关键词：

Best linear unbiased estimator; Maximum likelihood estimator; Moving extremes ranked set sampling;

D O I：

10.1080/03610918.2020.1853167

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, estimation of parameters alpha and beta for the log-extended exponential-geometric distribution will be respectively considered in cases when beta is known and when both are unknown. Simple random sampling (SRS) and moving extremes ranked set sampling (MERSS) will be used, and several traditional estimators will be considered. The estimators using MERSS are compared to the corresponding ones using SRS. The numerical results show that the estimators using MERSS are significantly more efficient than the ones using SRS. A real data set is used for illustration.

引用

页码：267 / 277

页数：11

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