论文标题
受访者驱动驱动的抽样和捕获征用的人口规模估计：统一框架
Population Size Estimation for Respondent-Driven Sampling and Capture-Recapture: A Unifying Framework
论文作者
论文摘要
本文介绍了受访者驱动驱动的抽样（RDS）的人口量的估计，这是一种链接追踪抽样的一种变体，这些变体利用社交网络在许多波浪上利用许多波浪来招募隐藏人口的个人。 RDS流程主要由可能报告已收到或给出的招聘建议或提名的个人参与者控制。通过考虑一段时间内给出或获得的所有提名，可以创建一个捕获扣除数据集，其中单位是至少获得一个提名和捕获场合的个人，即时间间隔或招聘浪潮，目的是估计隐藏人口的$ N $。在本文中，我们认为生成RDS提名数据的基本过程是捕获重新接收实验的基础过程。然后，我们提出了一种估计人口规模的方法，并调查了其与经典捕获征收假设不同的绩效。
This paper deals with the estimation of population sizes for respondent-driven sampling (RDS), a variant of link-tracing sampling that leverages social networks over a number of waves to recruit individuals from hidden populations. The RDS process is mostly controlled by individual participants who might report on recruitment proposals, or nominations, that they have received or given. By considering all nominations given or received over a time period, one can create a capture-recapture dataset in which units are individuals who have received at least one nomination and capture occasions are either time intervals or recruitment waves, with the goal of estimating the size $N$ of the hidden population. In this paper, we argue that the underlying process that generated the RDS nomination data is that of a capture-recapture experiment. We then proposed a methodology for the estimation of the population size and investigated its performance against departures from classical capture-recapture assumptions.
