This paper proposes a non-parametric Bayesian approach to detect the change-points of intensity rates in the recurrent-event context and cluster subjects by the change-points. Recurrent events are commonly observed in medical and engineering research. The event counts are assumed to follow a non-homogeneous Poisson process with piecewise-constant intensity functions. We propose a Dirichlet process mixture model to accommodate heterogeneity in subject-specific change-points. The proposed approach provides an objective way of clustering subjects based on the change-points without the need of pre-specified number of latent clusters or model selection procedure. A simulation study shows that the proposed model outperforms the existing Bayesian finite mixture model in detecting the number of latent classes. The simulation study also suggests that the proposed method is robust to the violation of model assumptions. We apply the proposed methodology to the Naturalistic Teenage Driving Study data to assess the change in driving risk and detect subgroups of drivers.

中文翻译：

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复发事件的非参数贝叶斯变点方法

本文提出了一种非参数贝叶斯方法来检测重复事件上下文中强度率的变化点，并通过变化点聚类主题。在医学和工程研究中经常观察到复发事件。假设事件计数遵循具有分段恒定强度函数的非齐次 Poisson 过程。我们提出了一个狄利克雷过程混合模型来适应特定主题变化点的异质性。所提出的方法提供了一种基于变化点对主题进行聚类的客观方法，而无需预先指定潜在聚类的数量或模型选择程序。仿真研究表明，所提出的模型在检测潜在类的数量方面优于现有的贝叶斯有限混合模型。模拟研究还表明，所提出的方法对于违反模型假设是鲁棒的。我们将提议的方法应用于自然主义青少年驾驶研究数据，以评估驾驶风险的变化并检测驾驶员亚群。