This article pertains to the analysis of recurrent event data in the presence of spatial correlation. Consider units located at n possibly spatially correlated geographical areas described by their longitude and latitude and monitored for the occurrence of an event that can recur. We propose a new class of semiparametric models for recurrent events that simultaneously account for risk factors and correlation among the spatial locations, and that subsumes the current methods. Since the parameters involved in the models are not directly estimable because of the high dimension of the likelihood, we use composite likelihood approach for estimation. The approach leads to estimates with population interpretation where their large sample properties are obtained under a reasonable set of regularity conditions. Simulation studies suggest that the resulting estimators have a very good finite sampling properties. The methods are illustrated using spatial data on recurrent esophageal cancer in the northern region of France and recurrent wildfire data in the province of Alberta, Canada.

中文翻译：

---

具有空间相关重复事件的半参数估计

本文涉及在存在空间相关性的情况下对周期性事件数据的分析。考虑位于n的单位可能在空间上相关的地理区域，由其经度和纬度描述，并监测可能再次发生的事件的发生。我们提出了一类新的周期性事件半参数模型，它同时考虑了空间位置之间的风险因素和相关性，并且包含了当前的方法。由于模型中涉及的参数由于似然性的高维而不能直接估计，因此我们使用复合似然法进行估计。该方法导致具有总体解释的估计，其中它们的大样本属性是在一组合理的规律性条件下获得的。模拟研究表明，得到的估计器具有非常好的有限采样特性。