基于通用M估计的有限人口平均值估计的强大方法
1Industrial Engineering Department, College of Engineering, University of Bisha, 61922, Bisha, Saudi Arabia. kabuhasel@ub.edu.sa.
Scientific reports
|January 12, 2026
概括
这项研究引入了用于调查采样的新型通用M估计 (GM估计) 技术,提供了可靠和高效的平均估计. 这些新方法显著优于传统估计器,特别是在受污染的数据中.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
背景情况:
- 在调查采样中的经典回归估计器由于异常值和模型偏差而容易出现效率低下和不稳定.
- 现有的方法难以有效处理受污染或异构的数据集.
研究的目的:
- 为有限种群平均值估计提出一类新的可靠回归类型估计器.
- 提高调查采样的稳定性和效率,特别是在存在异常值和模型偏差的情况下.
主要方法:
- 利用通用M估计 (GM估计) 框架来开发新的估计器.
- 整合了适应权重方案 (允许-GM,施威普斯-GM,SIS-GM) 以减轻异常效应.
- 在第一阶近似下,对偏差和平均平方误差 (MSE) 的衍生分析表达式.
主要成果:
- 与OLS和Huber估计器相比,GM型估计器的效率和稳定性明显更高.
- 在大量数据污染的模拟中,效率增长超过了150%.
- 拟议的估计器在各种调参数和相关结构中显示出强大的稳定性.
结论:
- 拟议的转基因估计方法为调查采样中平均估计提供了强大的和高效的替代方案.
- 这些估计器特别适用于受污染和异质数据环境.
- 该研究推进了用于调查数据分析的强有力的统计方法.
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