Respondent-driven sampling on directed networks
Respondent-driven sampling on directed networks
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DOI：
10.1214/13-ejs772
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发表时间：
2013-01-01
影响因子：
1.1
通讯作者：
Britton, Tom
中科院分区：
文献类型：
--
作者：
Lu, Xin;Malmros, Jens;Britton, Tom
响应者驱动抽样（RDS）是一种广泛使用的方法，用于从隐藏的人口中产生连锁参考样本。它是雪球抽样法的延伸，在满足某些假设的情况下，可以产生无偏的人口估计数。一个不太可能得到满足的关键假设是，招聘过程发生的熟人网络是无定向的，这意味着所有招聘人员都应该有可能被他们招聘的人招聘。使用平均场的方法，我们开发了一个估计，这是基于先验信息的估计变量的平均程度。当入度已知时，如在互联网社交网络上的RDS研究中，与现有方法相比，该估计器可以大大减少估计误差和偏差;当入度未知时，如在基于访谈的RDS研究中，该估计器可以通过敏感性分析作为一种工具来解释网络导向性的不确定性和自报度数据中的误差。新的估计器的性能，与以前的RDS估计，深入研究了不同结构的网络上的模拟。我们应用新的估计经验RDS研究注射吸毒者在纽约市。
Respondent-driven sampling (RDS) is a widely used method for generating chain-referral samples from hidden populations. It is an extension of the snowball sampling method and can, given that some assumptions are met, generate unbiased population estimates. One key assumption, not likely to be met, is that the acquaintance network in which the recruitment process takes place is undirected, meaning that all recruiters should have the potential to be recruited by the person they recruit. Using a mean-field approach, we develop an estimator which is based on prior information about the average indegrees of estimated variables. When the indegree is known, such as for RDS studies over internet social networks, the estimator can greatly reduce estimate error and bias as compared with current methods; when the indegree is not known, which is most common for interview-based RDS studies, the estimator can through sensitivity analysis be used as a tool to account for uncertainties of network directedness and error in self-reported degree data. The performance of the new estimator, together with previous RDS estimators, is investigated thoroughly by simulations on networks with varying structures. We have applied the new estimator on an empirical RDS study for injecting drug users in New York City.