Bayesian Poisson Log-normal Model with Regularized Time Structure for Mortality Projection of Multi-population
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.
Cite
@article{arxiv.2010.04775,
title = {Bayesian Poisson Log-normal Model with Regularized Time Structure for Mortality Projection of Multi-population},
author = {Zhen Liu and Xiaoqian Sun and Leping Liu and Yu-Bo Wang},
journal= {arXiv preprint arXiv:2010.04775},
year = {2021}
}