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The improvement of mortality projection is a pivotal topic in the diversebranches related to insurance, demography, and public policy. Motivated by thethread of Lee-Carter related models, we propose a Bayesian model to estimateand predict mortality rates for multi-population. This new model features ininformation borrowing among populations and properly reflecting variations ofdata. It also provides a solution to a long-time overlooked problem: modelselection for dependence structures of population-specific time parameters. Byintroducing a Dirac spike function, simultaneous model selection and estimationfor population-specific time effects can be achieved without much extracomputation cost. We use the Japanese mortality data from Human MortalityDatabase to illustrate the desirable properties of our model.

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