On stratified ranked set sampling for the quest of an optimal class of estimators

被引：5

作者：

Bhushan, Shashi ^{[1
]}

Kumar, Anoop ^{[2
]}

Hussam, Eslam ^{[3
]}

Mustafa, Manahil SidAhmed ^{[4
]}

Zakarya, Mohammed ^{[5
]}

Alharbi, Wedad R. ^{[6
]}

机构：

[1] Univ Lucknow, Dept Stat, Lucknow 226007, Uttar Pradesh, India

[2] Amity Univ Uttar Pradesh, Amity Sch Appl Sci, Dept Stat, Lucknow 226028, India

[3] Prince Sattam bin Abdulaziz Univ, Coll Business Adm Hawtat Bani Tamim, Dept Accounting, Al Kharj, Saudi Arabia

[4] Univ Tabuk, Fac Sci, Dept Stat, Tabuk, Saudi Arabia

[5] King Khalid Univ, Coll Sci, Dept Math, POB 9004, Abha 61413, Saudi Arabia

[6] Univ Jeddah, Coll Sci, Phys Dept, Jeddah 23890, Saudi Arabia

来源：

ALEXANDRIA ENGINEERING JOURNAL | 2024年 / 86卷

关键词：

Stratified ranked set sampling; Mean square error; Efficiency; AUXILIARY INFORMATION; PRODUCT ESTIMATORS; GENERAL PROCEDURE; MEAN ESTIMATORS; RATIO ESTIMATOR;

D O I：

10.1016/j.aej.2023.11.037

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In the sample survey theory, the crux of survey practitioners is to provide "accurate" estimators of the parameter of choice. The conventional theory depends on the regression/difference estimators as they correspond to the best linear unbiased (BLU) estimators. This paper suggests some optimal classes of estimators by modifying the conventional estimators under stratified ranked set sampling (SRSS). The characteristics of the suggested estimators are established to the first-order approximation. The performance of the suggested class of estimators under SRSS has been theoretically and experimentally shown to be superior to traditional estimators, particularly regression (BLU) estimators.

引用

页码：79 / 97

页数：19

共 44 条

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