通过建模潜在和观察到的异质性来估计封闭群体的大小
Francesco Bartolucci1, Antonio Forcina1
1Department of Economics, University of Perugia, 06123 Perugia, Italy.
Biometrics
|March 27, 2024
概括
这项研究引入了一种改进的经验概率 (EL) 方法,用于捕获-重新捕获数据,增强人口规模估计. 新方法提供了更高的效率,特别是对于较小的样本大小,改善了低估计估计.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 人口生态学 人口生态学
背景情况:
- 捕获-重新捕获方法对于估计种群大小至关重要.
- 现有的方法在处理复杂的数据结构 (如序列依赖) 时可能缺乏灵活性.
- 隐性类型模型为捕获概率中未观察到的异质性提供了一个框架.
研究的目的:
- 为捕获-重新捕获数据扩展经验概率 (EL) 方法.
- 纳入潜在类模型,允许串行依赖和共变量.
- 直接估计总人口规模,并改进低估计估计.
主要方法:
- 经验概率 (EL) 方法的扩展.
- 开发一个灵活的隐性类型模型家族.
- 费舍尔评分算法用于最大概率估计.
- 为非参数组件估计引入一种高效的替代方案.
主要成果:
- 证明共变量分布和未捕获概率之间的一比一,严格增加的关系.
- 用于参数估计的非对称结果的概述.
- 对人口大小的概率概率置信区间的发展.
- 模拟研究显示,低估的条件最大概率估计的效率优于低估的条件最大概率估计.
结论:
- 提出的基于经验概率的潜在类模型为捕获-重新捕获分析提供了一种灵活和高效的方法.
- 该方法提供了人口规模的直接估计和改进的不足估计,特别是在数据有限的场景中.
- 这些发现对生态和流行病学研究有影响,这些研究需要准确的人口规模估计.
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