A Bayesian Spatial Modeling Approach to Mortality Forecasting
Abstract
This paper extends Bayesian mortality projection models for multiple populations considering the stochastic structure and the effect of spatial autocorrelation among the observations. We explain high levels of overdispersion according to adjacent locations based on the conditional autoregressive model. In an empirical study, we compare different hierarchical projection models for the analysis of geographical diversity in mortality between the Japanese counties in multiple years, according to age. By a Markov chain Monte Carlo (MCMC) computation, results have demonstrated the flexibility and predictive performance of our proposed model.
Cite
@article{arxiv.2102.11501,
title = {A Bayesian Spatial Modeling Approach to Mortality Forecasting},
author = {Zhen Liu and Xiaoqian Sun and Yu-Bo Wang},
journal= {arXiv preprint arXiv:2102.11501},
year = {2021}
}
Comments
The corresponding author considers there is a huge drawback at the main methodology to revise in a long time and is not willing to be the duty of corresponding author. Hence, I request the publisher to withdraw this paper. Thanks