Scalable Model-Based Inference for Social Networks from Complex Sampling Designs

该项目将开发新的统计方法，用于分析难以研究的人群的社交网络。 这一新方法预计将导致更强大的和统计有效的推论，从数据难以研究的人口。 当难以找到所研究的人口时，传统的调查方法效果不佳。 典型的原因是，很难在更大的人口中确定人口中的个人，或者人口受到侮辱，因此个人不太可能参加调查。 在这些情况下，传统方法的应用非常昂贵。 这类人群的例子包括不受管制的工人、自营职业者、新移民、无家可归者和注射毒品使用者。 对于这些人口，调查人员开始采用利用人口中的社会关系网络的方法，以促进参与调查。 虽然这些方法是收集数据的有效方法，但从以这种方式收集的数据中得出科学有效的结论是一项挑战。 这一新方法将应用于一种流行的调查方法，例如，地球仪的公共卫生部门使用这种方法来估计艾滋病毒和其他疾病的发病率。 作为该项目的一部分开发的方法和软件有可能影响公共卫生单位估计的发病率和基于这些发病率的政策决定。 有关社交网络的数据既反映了新兴的社会结构，也反映了观察这些结构的透镜。 缺乏收集和分析网络数据的统计方法，从而无法了解这些社会结构的影响。 该项目将进一步开发一个基于模型的通用框架，用于在网络部分未被观察到的情况下分析社交网络。 该研究将为一类称为指数族随机网络模型（ERNM）的模型开发可扩展的，复合的，基于可能性的推理。这项工作将扩大这些模型的适用范围。 研究人员将开发一个基于ERNM的似然模型，用于响应者驱动抽样（RDS）和新的、更丰富的设计，如私有化网络抽样。 该模型将通过蒙特卡罗模拟研究在一系列网络规模进行验证。 还将对现有RDS数据集进行二次分析。 总的来说，研究将提供一个基础，在社会关系和/或个人特征不明显的情况下，由于抽样设计或无响应机制的科学估计。 这将有助于分析使用新的抽样设计收集的数据，新的抽样设计利用社会关系提高统计效率和稳健性。

This project will develop new statistical methodology for the analysis of social networks from hard-to-study populations. This new methodology is expected to result in more robust and statistically valid inferences from data on hard-to-study populations. Traditional approaches to surveys do not work well when the population under study is hard to find. Typical reasons are that the individuals within the population are hard to identify within a larger population, or the population is stigmatized and individuals therefore are less likely to participate in the survey. In these cases, traditional methods are very expensive to apply. Examples of such populations are unregulated workers, the self-employed, new migrants, the homeless, and injection drug users. For such populations, surveyors are starting to employ methods that use the network of social relations amongst the population to facilitate participation in the survey. While these methods are an effective way to collect data, it is a challenge to make scientifically valid conclusions from data collected in this way. This new methodology will be applied to a popular survey approach that is used, for example, in public health departments across the globe to estimate rates of HIV and other diseases. The methods and software developed as part of this project have the potential to impact the disease rates estimated by public health units and the policy decisions based on them. Data about social networks reflect both emerging social structures and the lens through which they are observed. There is a dearth of statistical methodology for the collection and analysis of network data that enable understanding the implications of these social structures. This project will further the development of a general model-based framework for the analysis of social networks in situations where the network is partially unobserved. The research will develop scalable, composite, likelihood-based inference for a class of models known as exponential-family random network models (ERNM). This work will increase the range of applicability of these models. The investigators will develop an ERNM-based likelihood model for respondent-driven sampling (RDS) and new, richer designs, such as privatized network sampling. The model will be validated via Monte Carlo simulation studies over a range of network sizes. Secondary analyses of existing RDS datasets also will be conducted. Overall, the research will provide a basis for scientific estimation in situations where the social relations and/or individual characteristics either are not evident due to the sampling design or non-response mechanisms. This will allow for the analysis of data collected using new sampling designs that use social relationships to improve statistical efficiency and robustness.