As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. Based on a simulation study, we investigate the properties of the estimation procedure. To demonstrate its usefulness in two distinct applications, we employ this model to combat events from the Syrian civil war and student co-location data. Results from the simulation and the applications identify the REMSE as a suitable approach to modeling relational event data in the presence of spurious events.

随着关系事件模型成为研究关系结构的越来越流行的模型，大规模事件数据收集的可靠性变得越来越重要。自动或人工编码的事件在事件识别中经常遭受不可忽略的错误发现率。大多数传感器数据主要基于参与者在预定义时间窗内的空间接近度；因此，观察到的事件可能与社会关系或随机共处有关。这两个例子都暗示了可能会使估计和推断产生偏差的虚假事件。我们提出了虚假事件的关系事件模型 (REMSE)，这是对现有交互数据方法的扩展。该模型为建模数据提供了一个灵活的解决方案，同时控制了虚假事件。我们的模型估计是通过数据扩充以经验贝叶斯方法进行的。基于模拟研究，我们研究了估计程序的特性。为了证明它在两个不同的应用程序中的有用性，我们使用这个模型来处理叙利亚内战和学生共同定位数据中的事件。模拟和应用程序的结果将 REMSE 确定为在存在虚假事件的情况下对关系事件数据建模的合适方法。