Bayesian Poisson Mortality Projections with Incomplete Data

TL;DR

This paper introduces an extended Bayesian Poisson model with a new sampling algorithm to improve mortality projections from incomplete data, demonstrated on Chinese male mortality data from 1995-2016.

Contribution

It develops a flexible time-structured Poisson model with an efficient MCMC algorithm that handles incomplete mortality data without imputation.

Findings

Model produces accurate mortality forecasts with incomplete data

Sampling algorithm improves MCMC convergence

Forecasts are comparable to those using imputed data

Abstract

The missing data problem pervasively exists in statistical applications. Even as simple as the count data in mortality projections, it may not be available for certain age-and-year groups due to the budget limitations or difficulties in tracing research units, resulting in the follow-up estimation and prediction inaccuracies. To circumvent this data-driven challenge, we extend the Poisson log-normal Lee-Carter model to accommodate a more flexible time structure, and develop the new sampling algorithm that improves the MCMC convergence when dealing with incomplete mortality data. Via the overdispersion term and Gibbs sampler, the extended model can be re-written as the dynamic linear model so that both Kalman and sequential Kalman filters can be incorporated into the sampling scheme. Additionally, our meticulous prior settings can avoid the re-scaling step in each MCMC iteration, and allow model selection simultaneously conducted with estimation and prediction. The proposed method is applied to the mortality data of Chinese males during the period 1995-2016 to yield mortality rate forecasts for 2017-2039. The results are comparable to those based on the imputed data set, suggesting that our approach could handle incomplete data well.