用模型平均方法对志愿者网络调查样本的估计
Zhan Liu1, Junbo Zheng1, Chaofeng Tu1
1Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China.
Journal of applied statistics
|November 16, 2023
概括
这项研究引入了一种新型的模型平均方法,以从网络调查中获得更准确的人口估计. 将后勤回归和增强模型结合起来,可以改善志愿者样本的倾向性得分估计.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
- 计算统计学 计算统计学
背景情况:
- 志愿者网络调查样本通常需要进行统计调整以准确估计人口.
- 倾向性评分方法被广泛使用,但对模型选择敏感.
- 由于不同的倾向得分模型,现有的方法产生了不同的人口估计.
研究的目的:
- 为志愿者网络调查样本开发更准确的人口估计方法.
- 通过组合参数和非参数模型来改进倾向得分估计.
- 确定拟议方法的理论特性和实际实施.
主要方法:
- 提出了一种对倾向性得分的模型平均估计方法.
- 使用了参数逻辑回归模型和非参数通用增强模型的估计值.
- 建立了拟议估计器的一致性和异常正常性.
- 开发了一个用于实现的计算算法.
主要成果:
- 拟议的模型平均方法在模拟研究中显示出更好的准确性.
- 该方法提供了一种结合不同倾向得分模型的可靠方法.
- 确定了估计器的理论性质 (一致性,非对称的正常性).
结论:
- 模型平均化倾向得分估计为志愿者网络调查提供了更可靠的方法.
- 与单一模型方法相比,拟议的方法提高了人口估计的准确性.
- 该方法通过模拟和现实世界调查数据集 (NSAS) 得到验证.
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