论文标题
带有假设测试程序的贝叶斯演员的多级关系事件模型
A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures
论文作者
论文摘要
关系事件网络数据越来越多。因此，此类数据的统计模型也浮出水面。这些模型主要集中在单个网络的分析上，而在许多应用程序中，观察到多个独立的事件序列，这些序列可能显示出相似的社交互动动态。此外，测试有关社会互动行为的假设的统计方法不发达。因此，当前论文的贡献是双重的。首先，我们提出了面向动态Actor的模型的多级扩展，该模型使研究人员可以分别建模发送者和接收器过程。多级公式可以使跨网络的信息有原则的概率借贷，以准确估计社会动态的驱动因素。其次，提出了一种灵活的方法来检验有关关系事件序列中关于常见和异质社交互动驱动因素的假设。儿童和教师教师之间的社交互动数据用于展示该方法。
Relational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models mainly focus on the analysis of single networks, while in many applications, multiple independent event sequences are observed, which are likely to display similar social interaction dynamics. Furthermore, statistical methods for testing hypotheses about social interaction behavior are underdeveloped. Therefore, the contribution of the current paper is twofold. First, we present a multilevel extension of the dynamic actor-oriented model, which allows researchers to model sender and receiver processes separately. The multilevel formulation enables principled probabilistic borrowing of information across networks to accurately estimate drivers of social dynamics. Second, a flexible methodology is proposed to test hypotheses about common and heterogeneous social interaction drivers across relational event sequences. Social interaction data between children and teachers in classrooms are used to showcase the methodology.
