Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.

中文翻译：

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根据受访者驱动的抽样数据进行网络模型辅助推理。

受访者驱动抽样是一种广泛使用的方法，通过社交网络上的链接跟踪对难以接触到的人群进行抽样。从此类数据中进行推断需要专门的技术，因为采样过程部分超出了研究人员的控制范围，并且部分是隐式定义的。因此，通常不可能直接计算传统基于设计的推理的采样权重，并且似然推理需要对复杂的采样过程进行建模。作为替代方案，我们引入了模型辅助方法，从而产生了利用工作网络模型的基于设计的估计器。我们推导出一类新的总体均值估计量和相应的引导标准误差估计量。与现有估计器相比，我们展示了改进的性能，包括对初始便利样本的调整。我们还将该方法和扩展应用于估计高危人群中的艾滋病毒流行率。