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.

翻译：本文研究针对受访者驱动抽样（RDS）的总体规模估计方法。RDS是一种链式追踪抽样的变体，通过利用社会网络在多个波次中招募隐藏人群的个体。RDS过程主要受个体参与者控制，他们可能会报告自己收到或发出的招募提议（即提名）。通过考虑给定时间段内所有发出或接收的提名，可以构建一个捕获-再捕获数据集：其中单元为至少收到一次提名的个体，捕获时机为时间间隔或招募波次，目标是估计隐藏人群的规模$N$。本文论证生成RDS提名数据的底层过程本质上是捕获-再捕获实验。我们随后提出了一种总体规模估计方法，并研究了该方法在偏离经典捕获-再捕获假设时的表现性能。