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

该项目将开发新的统计方法，用于从难以研究的人群中分析社会网络。这种新方法有望从难以研究的人群的数据中得出更可靠和统计上有效的推论。当研究对象很难找到时，传统的调查方法就行不通了。典型的原因是群体中的个体在更大的群体中很难被识别，或者群体被污名化，因此个体不太可能参与调查。在这些情况下，传统方法的应用非常昂贵。这类人群包括不受管制的工人、个体经营者、新移民、无家可归者和注射吸毒者。对于这样的人群，调查者开始采用利用人群之间的社会关系网络的方法来促进参与调查。虽然这些方法是收集数据的有效方式，但从这种方式收集的数据中得出科学有效的结论是一项挑战。这种新方法将应用于一种流行的调查方法，例如，全球公共卫生部门用于估计艾滋病毒和其他疾病的发病率。作为该项目的一部分而开发的方法和软件有可能影响公共卫生单位所估计的发病率以及据此作出的政策决定。关于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视角。目前缺乏收集和分析网络数据的统计方法，无法理解这些社会结构的含义。该项目将进一步开发一个通用的基于模型的框架，用于在网络部分未被观察的情况下分析社交网络。该研究将为一类被称为指数族随机网络模型（ERNM）的模型开发可扩展的、复合的、基于似然的推理。这项工作将增加这些模型的适用范围。研究人员将开发一种基于ernm的可能性模型，用于受访者驱动抽样（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上进行验证。还将对现有的RDS数据集进行二次分析。总体而言，该研究将为由于抽样设计或无反应机制而导致社会关系和/或个人特征不明显的情况下的科学估计提供基础。这将允许使用使用社会关系的新抽样设计来分析收集的数据，以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.