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arXiv · 2010.04775

Bayesian Poisson Log-normal Model with Regularized Time Structure for Mortality Projection of Multi-population

Zhen Liu·Xiaoqian Sun·Leping Liu·Yu-Bo Wang
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Abstract

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.

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Zhen Liu, Xiaoqian Sun, Leping Liu, Yu-Bo Wang. 2020-10-09. Bayesian Poisson Log-normal Model with Regularized Time Structure for Mortality Projection of Multi-population. https://arxiv.org/abs/2010.04775

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