Relational Event Models (REMs) provide a rigorous framework for analyzing dyadic interactions observed in continuous time, capturing history-dependent dynamics such as triadic closure and reciprocity. Framing REMs through the lens of counting processes embeds the model in a rich theoretical foundation, facilitating its mathematical analysis. While Maximum Likelihood Estimation (MLE) is standard practice for estimating these models, the underlying statistical guarantees rely on specific asymptotic regimes, namely, whether the network size (n), the observational period (T), or both approach infinity. We review the theoretical foundations of such counting-process-based models, formalizing the core assumptions required to achieve asymptotic normality across these different limits. With a specific focus on Cox-type multiplicative models, we detail the circumstances under which these assumptions hold. Supported by simulation studies, we illustrate how structural modeling choices, including temporal windowing and logarithmic transformations, affect empirical coverage and estimator convergence. We thereby derive several guiding principles for specifying such models in realistic contexts, bridging theory and practice.

翻译：关系事件模型（REMs）为分析连续时间中观测到的二元交互提供了严谨框架，能够捕捉三元闭合与互惠性等历史依赖动态。通过计数过程视角诠释REMs，将其嵌入丰富的理论基础中，从而促进其数学分析。尽管极大似然估计（MLE）是估计这些模型的标准方法，但其统计保障依赖于特定渐近机制，即网络规模（n）、观测周期（T）或两者共同趋于无穷。本文综述了此类基于计数过程模型的理论基础，形式化了在不同极限条件下实现渐近正态性所需的核心假设。聚焦于Cox型乘法模型，我们详细阐明了这些假设成立的条件。基于仿真研究，我们论证了时间窗口化与对数变换等结构性建模选择如何影响经验覆盖率和估计量收敛性。由此推导出在现实情境中规范此类模型的若干指导原则，架设起理论与实践的桥梁。