论文标题
网络规模的三十年
Thirty Years of The Network Scale up Method
论文作者
论文摘要
对于许多领域来说，估计难以到达的人群的大小是一个重要的问题。网络扩展方法（NSUM）是一种相对较新的方法来估算这些难以到达的人群的规模，通过向受访者询问受访者一个问题“您知道多少X”，X是X的X在哪里（例如，“您知道多少女性性别工作者？”）。这些问题的答案构成了汇总关系数据（ARD）。 NSUM已用于估计各种亚群的大小，包括女性性工作者，吸毒者，甚至是住院的儿童。在网络扩展方法中，有许多有关隐藏群体大小的估计器，包括直接估计器，最大似然估计器和贝叶斯估计器。在本文中，我们首先提供了对ARD属性和收集数据的技术的深入分析。然后，我们根据每个模型背后的假设，估计器之间的关系以及实施方法的实际考虑，全面审查了不同的估计方法。最后，我们提供了主要方法和广泛应用列表的摘要，并讨论了该领域的开放问题和潜在的研究方向。
Estimating the size of hard-to-reach populations is an important problem for many fields. The Network Scale-up Method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach populations by asking respondents the question, "How many X's do you know," where X is the population of interest (e.g. "How many female sex workers do you know?"). The answers to these questions form Aggregated Relational Data (ARD). The NSUM has been used to estimate the size of a variety of subpopulations, including female sex workers, drug users, and even children who have been hospitalized for choking. Within the Network Scale-up methodology, there are a multitude of estimators for the size of the hidden population, including direct estimators, maximum likelihood estimators, and Bayesian estimators. In this article, we first provide an in-depth analysis of ARD properties and the techniques to collect the data. Then, we comprehensively review different estimation methods in terms of the assumptions behind each model, the relationships between the estimators, and the practical considerations of implementing the methods. Finally, we provide a summary of the dominant methods and an extensive list of the applications, and discuss the open problems and potential research directions in this area.
