A novel proposed class of estimators under ranked set sampling: Simulation and diverse applications

M Yusuf; Najwan Alsadat; Balogun Oluwafemi Samson; Mahmoud Abd El Raouf; Hanan Alohali

doi:10.1016/j.heliyon.2023.e20773

A novel proposed class of estimators under ranked set sampling: Simulation and diverse applications

Heliyon. 2023 Oct 10;9(10):e20773. doi: 10.1016/j.heliyon.2023.e20773. eCollection 2023 Oct.

Authors

M Yusuf¹, Najwan Alsadat², Balogun Oluwafemi Samson³, Mahmoud Abd El Raouf⁴, Hanan Alohali⁵

Affiliations

¹ Helwan University, Faculty of Science, Mathematics Department, Cairo, Egypt.
² Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi Arabia.
³ Department of Computing, Faulty of Forestry and Technology, University of Eastern Finland, FI-70211, Kuopio, Finland.
⁴ Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria, Egypt.
⁵ Department of mathematics, College of science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.

Abstract

This study presents a novel enhanced exponential class of estimators for population mean under RSS by employing data on an auxiliary variable. The suggested estimators' mean square error (MSE) is calculated approximately at order one. The efficiency conditions that make the suggested enhanced exponential class of estimators superior to the traditional estimators are found. A simulation study using hypothetically drawn normal and exponential populations evaluates the execution of the suggested estimators. The findings demonstrate that the suggested estimators outperform their traditional equivalents. In addition, real data examples are examined to show how the proposed estimators can be implemented in various real life problems.

Keywords: 62D05; Efficiency; Exponential estimators; Simulation study.