Actuarial Bayesian nonparametric regression modelling for survival data
提出一种灵活的回归模型，用于分析养老金计划成员的个人死亡率特征，该模型通过狄利克雷过程引入个体随机效应，能捕捉复杂数据模式（如晚年死亡率减速），且比标准参数模型有更好的样本外表现。
Abstract This paper introduces a flexible regression model for the statistical analysis of the individual mortality profile of pension scheme members. The model incorporates individual-specific random effects, which follow a discrete distribution drawn from a Dirichlet Process, enhancing its adaptability to complex data structures. This results in a Dependent Dirichlet Process mixture model in the spirit of De Iorio et al. (Biometrics 65(3):762–771. https://doi.org/10.1111/j.1541-0420.2008.01166.x , 2009), which accommodates nonmonotonic relationships between covariates and the regression function. The application of the model is illustrated through the analysis of a mid-sized UK pension scheme dataset. The model shows the ability to capture complex features of the data, such as the late life mortality deceleration at no cost in terms of model parsimony, and an improved out-of-sample performance compared with standard parametric alternatives, making it particularly suitable for actuarial modelling applications.