Dependence-Robust Confidence Intervals for Capture-Recapture Surveys.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Accounts of Chemical Research Pub Date : 2022-12-08 eCollection Date: 2023-11-01 DOI:10.1093/jssam/smac031
Jinghao Sun, Luk Van Baelen, Els Plettinckx, Forrest W Crawford
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引用次数: 1

Abstract

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. When k-capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by a 2k contingency 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.

捕获-再捕获调查的依赖-稳健置信区间
捕获-再捕获(CRC)调查用于估计不能直接枚举成员的人口规模。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、冲突伤亡人数和贩运受害者人数。当获得k个捕获样本时,样本子集中的单位捕获计数自然由2k列联表表示,其中一个元素-未出现在任何样本中的个体数量-仍然未被观察到。在没有额外假设的情况下,人口规模无法确定(即,确定的点)。为了实现点识别,通常使用严格的样本间相关性假设。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,我们应用部分识别理论来表明,关于样本之间依赖性质的弱假设或定性知识可用于表征真实总体规模的非平凡置信集。我们使用两种方法在两两捕获概率的界限下构造置信集:测试反演自举置信区间和剖面似然置信区间。仿真结果表明,每种方法的置信集都经过了良好的校准。在一项广泛的现实世界研究中,我们将新方法应用于使用异质调查数据来估计比利时布鲁塞尔注射毒品人数的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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