Alharbi Randa, Mustafa Manahil SidAhmed, Al Mutairi Aned, Hussein Mohamed, Yusuf M, Elshenawy Assem, Nassr Said G
Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia.
Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Heliyon. 2023 Oct 24;9(11):e21427. doi: 10.1016/j.heliyon.2023.e21427. eCollection 2023 Nov.
When measuring the research variable is complicated, expensive, or problematic, median ranked set sampling (MRSS) is often utilized since it is straightforward to rank the components using a low-cost sorting criterion. Using this sampling scheme, many authors considered the problem of population mean estimation with a single auxiliary variable in order to obtain more precised estimators than the traditional ratio type regression estimators. In this article, we extend their ideas based on regression approach using two auxiliary variables and introduce a new regression-type estimator along with its theoretical expression of minimum mean square error (MSE). The suggested estimator's applicability is demonstrated using both simulated and real-world data sets.
当测量研究变量复杂、昂贵或存在问题时，常常采用中位数排序集抽样（MRSS），因为使用低成本排序标准对样本进行排序很简单。使用这种抽样方案，许多作者考虑了利用单个辅助变量估计总体均值的问题，以便获得比传统比率型回归估计量更精确的估计量。在本文中，我们基于使用两个辅助变量的回归方法扩展了他们的想法，并引入了一种新的回归型估计量及其最小均方误差（MSE）的理论表达式。通过模拟数据集和真实数据集证明了所提估计量的适用性。
