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用量子变换进行中位数估计:用于分层两相采样的应用
Fatimah A Almulhim1, Hassan M Aljohani2
1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
新的基于五分位数的中位数估计器提高了分层采样的准确性和稳定性. 这些方法为实际中位数估计提供了更高的精度和有效性,特别是在有偏差数据的情况下.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
背景情况:
- 传统的中位数估计器通常假定正常,对异常值敏感.
- 这种敏感性限制了它们在现实应用中的可靠性,这些应用具有非正常或偏斜的数据.
研究的目的:
- 引入新的基于五分位数的中位数估计器.
- 为了提高分层两相采样的准确性和稳定性.
- 使用辅助数据提高中位数估计的效率.
主要方法:
- 在分层两相采样框架内使用了转化方法.
- 开发了基于五分位数的中位数估计器.
- 通过一级近似来导出偏差和平均平方误差 (MSE) 表达式.
- 使用 MSE 评估估计器效率.
主要成果:
- 建议的估计器在偏斜分布下的模拟中表现出卓越的性能.
- 对真实人口数据集的分析证实了新方法的有效性.
- 与现有方法相比,基于五分位数的估计器实现了更高的精度和效率.
结论:
- 新型基于五分位数的中位数估计器对于实际应用来说是强大而准确的.
- 这些估计器为中位数估计提供了更有效的替代方案,特别是在分层采样场景中.
- 这些方法提高了辅助数据的实用性,并且在异质层面上表现良好.
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