AI 中文总结
该研究针对关系事件模型的过拟合问题，通过模拟对比了未正则化ML估计、ABR和EBR的性能，发现带马蹄形先验的ABR和EBR变量选择与预测表现更优，推荐使用合适先验的ABR。
AI 中文摘要
关系事件模型（Relational Event Models, REMs）为纵向社交网络的动态变化提供了有价值的见解。然而，二元组事件率的潜在预测因子数量庞大，存在选择无关变量并指定出无法泛化到新数据的过拟合模型的风险。尽管贝叶斯正则化方法近来已被广泛用于解决该问题，但目前尚未有针对精确贝叶斯正则化（Exact Bayesian Regularization, EBR）和近似贝叶斯正则化（Approximate Bayesian Regularization, ABR）与REM常用的标准最大似然（Maximum Likelihood, ML）方法的性能进行系统评估。为解决这一问题，我们开展了一项模拟研究，生成具有不同强度内生和外生效应的有向关系事件历史（Relational Event History, REH）数据，比较三类方法的性能：（a）未正则化的ML估计；（b）通过似然的正态近似结合岭先验（Ridge）和马蹄形先验（Horseshoe）的ABR；（c）结合马蹄形先验的EBR。研究发现，对采用马蹄形先验的ABR和EBR应用基于阈值的标准，在变量选择准确性上优于使用ML估计p值的单变量选择；在预测性能方面，结合岭先验的ABR和结合马蹄形先验的EBR优于ML REM，尤其在小样本量下优势显著。考虑到EBR虽性能略优但计算负担大，因此建议研究人员使用结合合适先验的ABR。
英文摘要
Relational Event Models (REMs) provide valuable insights into the dynamics of longitudinal social networks. Yet, the vast availability of potential predictors for a dyad's event rate poses the risk of selecting irrelevant variables and specifying an overfitted model that does not generalize to new data. Despite the recent popularity of Bayesian regularization methods to address this, there has not been a systematic evaluation of the performance of Exact Bayesian Regularization (EBR) and Approximate Bayesian Regularization (ABR) against standard Maximum Likelihood (ML) procedures commonly used for REMs. To address this, we conduct a simulation study in which directed Relational Event History (REH) data is generated with endogenous and exogenous effects of varying strength and compare the performance of (a) unregularized ML estimation, (b) ABR through normal approximations of the likelihood with Ridge and Horseshoe priors, and (c) EBR with Horseshoe priors. We find that threshold-based criteria applied to ABR and EBR with Horseshoe priors outperform univariate selection using p-values from ML estimation in variable selection accuracy. Regarding predictive performance, ABR with Ridge priors and EBR with Horseshoe priors outperform ML REMs, particularly for small sample sizes. Given only small gains with large computational burdens when using EBR, we therefore advise researchers to use ABR with a suitable prior.
