论文标题
带有正则时间结构的贝叶斯泊松对数正态模型，用于多人群的死亡率投影
Bayesian Poisson Log-normal Model with Regularized Time Structure for Mortality Projection of Multi-population
论文作者
论文摘要
在与保险，人口统计学和公共政策有关的各种分支机构中，死亡率预测的改善是一个关键的话题。在Lee-Carter相关模型的线程中，我们提出了一个贝叶斯模型，以估计和预测多人群的死亡率。这个新模型在人群之间借贷以及正确反映数据变化的信息中具有特征。它还为长期被忽视的问题提供了解决方案：用于特定于人群特定时间参数的依赖性结构的模型选择。通过引入狄拉克峰值功能，可以在没有太多额外的计算成本的情况下实现同时的模型选择和特定于人群时间效应的估计。我们使用来自人类死亡率数据库的日本死亡率数据来说明我们的模型的理想特性。
The improvement of mortality projection is a pivotal topic in the diverse branches related to insurance, demography, and public policy. Motivated by the thread of Lee-Carter related models, we propose a Bayesian model to estimate and predict mortality rates for multi-population. This new model features in information borrowing among populations and properly reflecting variations of data. It also provides a solution to a long-time overlooked problem: model selection for dependence structures of population-specific time parameters. By introducing a Dirac spike function, simultaneous model selection and estimation for population-specific time effects can be achieved without much extra computation cost. We use the Japanese mortality data from Human Mortality Database to illustrate the desirable properties of our model.
