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Optimizing population mean estimation under ranked set sampling with applications to Engineering

Year: 2024
Type: article
Abstract: This article aims to develop two novel mean estimators of finite populations under ranked set sampling. The suggested estimators result in a high-efficiency gain due to their efficient formulation by integrating the classical estimators. We also discuss the theoretical properties and derive bias and mean square error up to the first order of approximation. To theoretically validate our proposed estimators, we derive the conditions under which our estimators prevail over the competing estimators. These conditions hold for all real data types, specifically in engineering applications, such as environmental monitoring, resource management, and quality control if the survey variable positively correlates with the supplementary variable.The findings suggest that when RSS is implemented effectively, it saves cost and improves precision, serving as a potent instrument for improved decision-making and resource management. An empirical and simulation study has been conducted using real and synthetic data to validate our methodology empirically. The suggested estimators offer a significant contribution to survey sampling in estimating mean under ranked set sampling.
DOI: 10.1016/j.rineng.2024.103064
Language: English
Open Access status: gold
Estimated APC paid: $1,490
OpenAlex ID: W4403157592
FWCI: 1.975
Cites: 26
Cited by: 6
Related to: 10