Dependence-Robust Confidence Intervals for Capture-Recapture Surveys.
Dependence-Robust Confidence Intervals for Capture-Recapture Surveys.
复制标题
捕获-再捕获调查的依赖性稳健置信区间。
DOI：
10.1093/jssam/smac031
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发表时间：
2023
影响因子：
2.1
通讯作者：
Crawford,ForrestW
中科院分区：
文献类型：
--
作者：
Sun,Jinghao;VanBaelen,Luk;Plettinckx,Els;Crawford,ForrestW
捕获-再捕获（CRC）调查用于估计其成员无法直接计数的人口规模。CRC调查已被用于估计2019冠状病毒病（COVID-19）感染人数，吸毒者，性工作者，冲突伤亡人员和贩运受害者。当获得k捕获样本时，样本子集中的单位捕获计数自然地由一致性表表示，其中一个元素-没有样本中出现的个体数-保持未观察。在没有额外假设的情况下，人口规模无法确定（即，点识别）。对样本之间的相关性的严格假设通常用于实现点识别。然而，现实世界的CRC调查经常使用便利样本，其中假设的依赖性不能得到保证，并且在这些假设下的人口规模估计可能缺乏经验可信度。在这项工作中，我们应用部分识别的理论表明，弱假设或定性知识样本之间的依赖性的性质，可以用来表征一个非平凡的置信集的真实人口规模。我们使用两种方法：测试反演自举置信区间和轮廓似然置信区间来构造成对捕获概率的置信区间。仿真结果表明，良好校准的置信度为每种方法。在一个广泛的现实世界的研究中，我们采用新的方法来使用异构的调查数据，以估计在比利时布鲁塞尔注射毒品的人数的问题。
Capture–recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims. Whenk-capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by acontingency table in which one element—the number of individuals appearing in none of the samples—remains unobserved. In the absence of additional assumptions, the population size is not identifiable (i.e., point identified). Stringent assumptions about the dependence between samples are often used to achieve point identification. However, real-world CRC surveys often use convenience samples in which the assumed dependence cannot be guaranteed, and population size estimates under these assumptions may lack empirical credibility. In this work, we apply the theory of partial identification to show that weak assumptions or qualitative knowledge about the nature of dependence between samples can be used to characterize a nontrivial confidence set for the true population size. We construct confidence sets under bounds on pairwise capture probabilities using two methods: test inversion bootstrap confidence intervals and profile likelihood confidence intervals. Simulation results demonstrate well-calibrated confidence sets for each method. In an extensive real-world study, we apply the new methodology to the problem of using heterogeneous survey data to estimate the number of people who inject drugs in Brussels, Belgium.