  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.
