Loading...
Please wait, while we are loading the content...
Bayesian Mortality Forecasting with Overdispersion
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wong, Jackie S. T. Forster, Jonathan J. Smith, Peter W. F. |
| Copyright Year | 2016 |
| Abstract | The ability to produce accurate mortality forecasts, accompanied by a set of representative uncertainty bands, is crucial in the planning of public retirement funds and various life-related businesses. In this paper, we focus on one of the drawbacks of the Poisson Lee-Carter model that imposes mean-variance equality, restricting the mortality variations across individuals. Specifically, we present two models to potentially account for overdispersion. They are fitted within a Bayesian paradigm for coherency. Markov Chain Monte Carlo (MCMC) methods are implemented to carry out parameter estimation. Several comparisons are made with the Bayesian Poisson Lee-Carter model to highlight the importance of accounting for overdispersion. We demonstrate that the methodology we developed prevents over-fitting and yields better calibrated prediction intervals for the purpose of mortality projections. Bridge sampling is used to approximate the marginal likelihood of each candidate model to perform model determination. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://eprints.soton.ac.uk/397979/1/paper.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm Bands Estimation theory Marginal model Markov chain Monte Carlo Monte Carlo method Overfitting Parameter (computer programming) Population Parameter Programming paradigm Projections and Predictions Sample Variance Sampling (signal processing) Seizures |
| Content Type | Text |
| Resource Type | Article |
