In this paper, we consider estimation of dynamic models of recurrent events (event histories) in continuous time using censored data. We develop maximum simulated likelihood estimators where missing data are integrated out using Monte Carlo and importance sampling methods. We allow for random effects and integrate out this unobserved heterogeneity using a quadrature rule. In Monte Carlo experiments, we find that maximum simulated likelihood estimation is practically feasible and performs better than both listwise deletion and auxiliary modelling of initial conditions. In an empirical application, we study ischaemic heart disease events for male Maoris in New Zealand.

中文翻译：

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用删失数据估计复发事件的动态模型

在本文中，我们考虑使用删失数据在连续时间内估计重复事件（事件历史）的动态模型。我们开发了最大模拟似然估计器，其中使用蒙特卡罗和重要性采样方法整合了缺失数据。我们允许随机效应并使用正交规则整合这种未观察到的异质性。在蒙特卡罗实验中，我们发现最大模拟似然估计实际上是可行的，并且比列表删除和初始条件的辅助建模表现更好。在实证应用中，我们研究了新西兰男性毛利人的缺血性心脏病事件。