为受访者驱动的样本采集模拟可见性分布,并将其应用于人口规模估计
Katherine R McLaughlin1, Lisa G Johnston2, Xhevat Jakupi3
1Department of Statistics, Oregon State University.
The annals of applied statistics
|January 7, 2026
概括
受访者驱动的抽样 (RDS) 可以通过使用新的"可见性"模型来改善隐藏的人口估计. 这种方法解决了来自自我报告的网络大小的偏差,增强了人口规模和流行率估计.
科学领域:
- 统计 统计 统计 统计
- 流行病学 流行病学
- 社交网络分析 社交网络分析
背景情况:
- 受访者驱动采样 (RDS) 对于研究隐藏群体至关重要,但依赖于自我报告的网络大小,容易产生偏见.
- 当前的RDS估计器使用自我报告的网络大小 (程度) 估计了包含概率,从而导致潜在的不准确性.
研究的目的:
- 增强RDS数据的连续采样人口规模估计 (SS-PSE) 框架.
- 引入"可见性"测量错误模型,以取代不可靠的自我报告的网络大小.
- 从RDS.提升人口规模和流行率估计的准确性.
主要方法:
- 开发了一个增强的SS-PSE框架,包含参与者"可见性"的测量错误模型.
- 模拟了参与者可以招募的个人数量.
- 将可见性SS-PSE框架应用于科索沃三个人口的RDS数据.
主要成果:
- 可见性模型有效地平滑了度分布,并处理缺失/无效的网络大小数据.
- 在现实世界RDS数据上展示了增强的SS-PSE框架的性能.
- 推断可见性提供了一个比自我报告的网络大小更强大的衡量标准.
结论:
- 拟议的可见性建模框架为RDS提供了相对于传统SS-PSE方法的显著改进.
- 这种方法可以减轻隐藏人口研究中与自我报告的网络大小相关的偏见.
- 该框架显示了在未来的研究中扩展到流行率估计的潜力.
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