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在分层采样中使用调查成本构建高效的层级边界

Karuna G Reddy¹, M G M Khan¹

¹School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Suva, Fiji.

|November 15, 2023

概括

本研究引入了一种新的方法,用于确定分层采样中最佳的层边界 (OSB) 和样本大小 (OSS),特别是当调查成本是固定的时. 这种方法提高了估计人口参数 (如收入) 的精度.

科学领域:

统计统计统计统计
调查方法调查方法
计量经济学计量经济学

背景情况:

分层抽样设计的目的是准确估计人口参数.
最佳的层边界 (OSB) 和样本大小 (OSS) 对于最大化精度至关重要.
在固定的总样本大小或预算限制下,现有的OSB/OSS方法可能不会保持最佳状态.

研究的目的:

提出一种计算OSB和OSS的方法,考虑每单位层测量成本或概率密度函数.
为了应对在固定调查预算下保持最佳分层的挑战.
在分层调查中提高人口参数估计的准确性.

主要方法:

开发了一种用于计算OSB和OSS的新方法,其中包含成本信息.
采用基于设计的分层,使用HILDA调查数据集 (第18波).
估计了澳大利亚每年的总可支配收入的平均值.
提供了数值插图,以指数和直角三角分布.

主要成果:

拟议的OSB和OSS计算方法即使具有固定的总样本大小和已知的成本,也是有效的.
使用HILDA数据的实证应用证明了该方法对收入等与政策相关的变量有用.
该技术在更新的stratifyR包中实现了实际应用.

结论:

建议的方法提供了令人满意的结果,为现有技术提供了改进的或可比的效率.
这种方法提高了分层调查中人口参数估计的准确性.
该方法在不同的连续变量分布和实际调查约束中是稳健的.

关键词:

62-11-11 这是一个很好的例子.62D05 这是一个很好的例子.62P2525 这是一个很好的例子.91B82 它们是什么?动态编程是一种动态编程.最好的分层是最优的分层.样本分配样本分配分层随机采样分层随机采样调查成本调查成本调查成本

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