在两种样本捕获-重新捕获研究中,对人口规模估计的模型选择和模型稳定性
Gracia Y Dong1,2, Jennifer McNicho3, Laura L E Cowen4
1Human Biology Program, University of Toronto, Toronto, Ontario, Canada.
American journal of epidemiology
|January 13, 2025
概括
林肯-彼得森估计器经常用于捕获-重新捕获研究,但这项研究发现它和其他模型都在与错误规范作斗争. 阿卡伊克信息标准 (AIC) 无法在仅有两次捕获时选择正确的模型.
科学领域:
- 生态生态学 生态生态学
- 流行病学 流行病学
- 统计 统计 统计 统计
背景情况:
- 在生态和流行病学研究中,采用两样采集-重新采集的研究很普遍.
- 林肯-彼得森估计器是最常用的分析方法,特别是在流行病学中.
- 当处理复杂的捕获概率因子时,目前的方法存在局限性.
研究的目的:
- 为了评估林肯-彼得森估计器与Huggins和Pledger的封闭人口方法的性能.
- 在各种条件下评估Akaike信息标准 (AIC) 在模型选择中的有效性.
- 调查模型错误规范对人口规模估计的影响.
主要方法:
- 林肯-彼得森的比较分析,Huggins的条件概率和Pledger的概率方法.
- 模拟研究以评估模型性能与时间,行为效应,以及捕获概率的异质性.
- 评估AIC的模型选择能力与有限的捕获场合.
主要成果:
- 检查的封闭人群模型表明,缺乏对模型错误规范的稳定性.
- 当只有两个捕获场合可用时,AIC无法准确地选择正确的模型.
- 模型的错误规范显著影响了人口大小估计的准确性.
结论:
- 标准的捕获-重新捕获模型,包括林肯-彼得森估计器,对错误规范很敏感.
- 在只有两次的捕获-重新捕获研究中,AIC不是一个可靠的模型选择工具.
- 仔细考虑潜在的错误规范对于在生态和流行病学研究中准确估计人口至关重要.
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