排序集抽样下一类新的拟议估计量：模拟与多样应用。
A novel proposed class of estimators under ranked set sampling: Simulation and diverse applications.
作者信息
Yusuf M, Alsadat Najwan, Oluwafemi Samson Balogun, El Raouf Mahmoud Abd, Alohali Hanan
机构信息
Helwan University, Faculty of Science, Mathematics Department, Cairo, Egypt.
Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi Arabia.
出版信息
Heliyon. 2023 Oct 10;9(10):e20773. doi: 10.1016/j.heliyon.2023.e20773. eCollection 2023 Oct.
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.
本研究通过利用辅助变量的数据，提出了一种在残差平方和（RSS）下用于总体均值估计的新型增强指数类估计量。所建议估计量的均方误差（MSE）近似计算到一阶。找到了使所建议的增强指数类估计量优于传统估计量的效率条件。使用假设抽取的正态和指数总体进行的模拟研究评估了所建议估计量的性能。结果表明，所建议的估计量优于其传统同类估计量。此外，还研究了实际数据示例，以展示所提出的估计量如何在各种实际问题中得到应用。
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