在复杂调查采样中填写矩阵
Xiaojun Mao1, Zhonglei Wang2, Shu Yang3
1School of Mathematical Sciences, Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
Annals of the Institute of Statistical Mathematics
|August 30, 2023
概括
本研究引入了一种新的矩阵完成方法,用于复杂的调查采样,以解决多变量非响应问题. 新方法提高了估计准确度,在健康状况评估模拟中表现优于现有方法.
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:
- 多变量非响应在复杂的调查采样中是一个重大挑战,如果忽视,就会导致有偏见的推断.
- 现有的归算方法往往侧重于行wise或列wise方法,未能利用完整的数据结构.
研究的目的:
- 为复杂的调查采样提出一种新的矩阵完成方法,同时利用行和列模式.
- 在复杂的调查设计下开发一个计算效率高的参数识别和估计方法.
- 为感兴趣的参数提供一个可靠的估计器,并得到一个误差限制.
主要方法:
- 使用列空间分解模型,将数据矩阵作为一个整体来捕获同时的行和列模式.
- 对于有限的人口,使用一个包含人口共变量的低级结构矩阵.
- 一个增强的逆概率权重估计器用于参数估计.
主要成果:
- 与模拟研究中的现有竞争对手相比,拟议的矩阵完成方法在较小的平均平方误差下表现出卓越的性能.
- 拟议方法的衍生差异估计器表现良好,表明可靠性.
- 该方法已成功地应用于评估美国人口的健康状况.
结论:
- 这种新的矩阵完成方法有效地解决了复杂调查中的多变量非响应问题,通过利用整个数据矩阵.
- 拟议的方法为调查数据提供了更好的估计准确性和可靠的差异估计.
- 这种方法为分析复杂的调查数据提供了有价值的工具,正如其应用于美国健康状况评估所证明的那样.
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