We develop a regression model for the analysis of competing risk events. The joint distribution of the time to these events is flexibly characterized by a random effect which follows a discrete probability distribution drawn from a Dirichlet Process, explaining their variability. This entails an additional layer of flexibility of this joint model, whose inference is robust with respect to the misspecification of the distribution of the random effects. The model is analysed in a fully Bayesian setting, yielding a flexible Dirichlet Process Mixture model for the joint distribution of the time to events. An efficient MCMC sampler is developed for inference. The modelling approach is applied to the empirical analysis of the surrending risk in a US life insurance portfolio previously analysed by . The approach yields an improved predictive performance of the surrending rates.

中文翻译：

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用于分析竞争风险事件的狄利克雷过程混合回归模型

我们开发了一个回归模型来分析竞争风险事件。这些事件的时间联合分布由随机效应灵活地表征，该随机效应遵循从狄利克雷过程中得出的离散概率分布，解释了它们的变异性。这需要该联合模型具有额外的灵活性，其推理对于随机效应分布的错误指定是稳健的。该模型在完全贝叶斯设置中进行分析，生成灵活的狄利克雷过程混合模型，用于事件时间的联合分布。开发了一种用于推理的高效 MCMC 采样器。该建模方法应用于先前由 分析的美国人寿保险投资组合中的退保风险的实证分析。该方法提高了投降率的预测性能。