Novel imputation methods under stratified simple random sampling

被引：3

作者：

Kumar, Anoop ^{[1
]}

Bhushan, Shashi ^{[2
]}

Mustafa, Manahil SidAhmed ^{[3
]}

Aldallal, Ramy ^{[4
]}

Aljohani, Hassan M. ^{[5
]}

Almulhim, Fatimah A. ^{[6
]}

机构：

[1] Cent Univ Haryana, Dept Stat, Mahendergarh 123031, India

[2] Univ Lucknow, Dept Stat, Lucknow 226007, UP, India

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

[4] Prince Sattam Bin Abdulaziz Univ, Coll Business Adm Hawtat Bani Tamim, Dept Management, Al Hulwah, Saudi Arabia

[5] Taif Univ, Coll Sci, Dept Math & Stat, POB 11099, Taif 21944, Saudi Arabia

[6] Princess Nourah Bint Abdulrahman Univ, Coll Sci, Dept Math Sci, POB 84428, Riyadh 11671, Saudi Arabia

来源：

ALEXANDRIA ENGINEERING JOURNAL | 2024年 / 95卷

关键词：

Missing values; Imputation; Stratified simple random sampling; ESTIMATORS; RATIO;

D O I：

10.1016/j.aej.2024.03.088

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called 'novel'. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively.

引用

页码：236 / 246

页数：11

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