在中位数排序集采样中增强平均估计器,使用双辅助信息
Randa Alharbi1, Manahil SidAhmed Mustafa1, Aned Al Mutairi2
1Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia.
Heliyon
|November 13, 2023
概括
中位数排序集采样 (MRSS) 通过使用辅助变量来改善人口平均值的估计. 本研究引入了一种新的回归类型估计器,具有两个辅助变量,以提高统计分析的精度.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
- 统计推理 统计推理
背景情况:
- 当直接测量很困难时,中位数排序集采样 (MRSS) 是有效的.
- 现有的方法通常使用一个辅助变量来估计人口平均值.
- 在复杂的场景中需要更精确的估计器.
研究的目的:
- 开发一种新的回归类型估计器,使用MRSS框架内的两个辅助变量.
- 从理论上推导出拟议估计器的最小平均平方误差 (MSE).
- 评估新估计器的实际表现.
主要方法:
- 扩展现有的MRSS估计技术.
- 开发一种新的回归类型估计器,其中包含两个辅助变量.
- 最小平均平方误差 (MSE) 的理论推导.
- 通过模拟研究和现实数据分析进行验证.
主要成果:
- 拟议的回归类型估计器与现有方法相比显示出更高的精度.
- 理论上的MSE计算支持估计器的效率.
- 来自模拟和真实数据的经验结果证实了估计器的实际适用性和优越性.
结论:
- 这种基于MRSS的新型回归估计器具有两个辅助变量,为人口平均值估计提供了更精确的方法.
- 当直接测量具有挑战性或昂贵时,这种方法特别有价值.
- 这些发现有助于推进统计抽样技术,以改善数据分析.
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