在分层随机抽样中探索混合物估计器
Kanwal Iqbal1,2, Syed Muhammad Muslim Raza1,3, Tahir Mahmood4
1Department of Economics and Statistics, Dr Hasan Murad School of Management (HSM), University of Management and Technology, Lahore, Pakistan.
PloS one
|September 17, 2024
概括
本研究引入了一种新的混合估计器,用于估计人口平均值,使用分层采样下的辅助变量. 拟议的方法提高了各种分布和样本大小的精度,超过了现有的估计器.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
- 数据科学数据科学数据科学
背景情况:
- 现代传感器技术能够产生大量的数据,需要高效的统计方法.
- 辅助变量 (定量和质量) 经常与研究变量一起记录,以获得成本效益.
- 混合估计器对于利用辅助信息来估计人口平均值是有价值的.
研究的目的:
- 为分层采样提出一种混合物估计器的一般化家族.
- 为了提高人口平均值估计的精度,使用辅助变量.
- 分析不同样本大小和分布的拟议估计者的行为.
主要方法:
- 在分层采样下开发通用混合物估计器.
- 对对称和不对称分布的估计器效率的研究.
- 对不同样本大小的正常分布的估计器趋同的分析.
主要成果:
- 拟议的通用混合物估计器与现有方法相比,显示出更高的精度.
- 估计器的性能在正常,均,韦布尔和马分布中得到验证.
- 估计器遵循Cauchy分布的样本大小<35,然后趋于正常.
结论:
- 拟议的通用混合估计器在估计人口平均值方面提供了显著的改进.
- 这些发现得到了健康和金融领域的现实应用的支持.
- 该研究强调了考虑样本大小和数据分布在估计器选择中的重要性.
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