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Bayesian estimation and model comparison for mortality forecasting

Bayesian estimation and model comparison for mortality forecasting
Bayesian estimation and model comparison for mortality forecasting
The ability to perform mortality forecasting accurately is of considerable interest for a wide variety of applications to avoid adverse costs. The recent decline in mortality poses a major challenge to various institutions in their attempts to forecast mortality within acceptable risk margins. The ultimate aim of our project is to develop a methodology to produce accurate mortality forecasts, with carefully calibrated probabilistic intervals to quantify the uncertainty encountered during the forecasts. Bayesian methodology is mainly implemented throughout the thesis for various benefits, but primarily due to its ability to provide a coherent modelling framework. Our contributions in this thesis can be divided into several parts. Firstly, we focus on the Poisson log-bilinear model by Brouhns et al. (2002), which induces an undesirable property, the mean-variance equality. A Poisson log-normal and a Poisson gamma log bilinear models, fitted using arbitrarily dffuse priors, are presented as possible solutions. We demonstrate that properly accounting for overdispersion prevents over-fitting and offers better calibrated prediction intervals for mortality forecasting. Secondly, we carry out Bayesian model determination procedures to compare the models, using marginal likelihoods computed by bridge sampling (Meng and Wong, 1996). To achieve our goal of approximating the marginal likelihoods accurately, a series of simulation studies is conducted to investigate the behaviour of the bridge sampling estimator. Next, a structurally simpler model which postulates a log-linear relationship between the mortality rate and time is considered. To provide a fair comparison between this model and the log-bilinear model, we carry out rigorous investigations on the prior specifications to ensure consistency in terms of the prior information postulated for the models. We propose to use Laplace prior distributions on the corresponding parameters for the loglinear model. Finally, we demonstrate that the inclusion of cohort components is crucial to yield more accurate projections and to avoid unnecessarily wide prediction intervals by improving the calibration between data signals and errors.
University of Southampton
Wong, Jackie Siaw Tze
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Wong, Jackie Siaw Tze
bc6647ec-62bc-4b36-b883-2afe7c529f65
Forster, Jonathan
e3c534ad-fa69-42f5-b67b-11617bc84879
Smith, Peter W F
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Wong, Jackie Siaw Tze (2017) Bayesian estimation and model comparison for mortality forecasting. University of Southampton, Doctoral Thesis, 259pp.

Record type: Thesis (Doctoral)

Abstract

The ability to perform mortality forecasting accurately is of considerable interest for a wide variety of applications to avoid adverse costs. The recent decline in mortality poses a major challenge to various institutions in their attempts to forecast mortality within acceptable risk margins. The ultimate aim of our project is to develop a methodology to produce accurate mortality forecasts, with carefully calibrated probabilistic intervals to quantify the uncertainty encountered during the forecasts. Bayesian methodology is mainly implemented throughout the thesis for various benefits, but primarily due to its ability to provide a coherent modelling framework. Our contributions in this thesis can be divided into several parts. Firstly, we focus on the Poisson log-bilinear model by Brouhns et al. (2002), which induces an undesirable property, the mean-variance equality. A Poisson log-normal and a Poisson gamma log bilinear models, fitted using arbitrarily dffuse priors, are presented as possible solutions. We demonstrate that properly accounting for overdispersion prevents over-fitting and offers better calibrated prediction intervals for mortality forecasting. Secondly, we carry out Bayesian model determination procedures to compare the models, using marginal likelihoods computed by bridge sampling (Meng and Wong, 1996). To achieve our goal of approximating the marginal likelihoods accurately, a series of simulation studies is conducted to investigate the behaviour of the bridge sampling estimator. Next, a structurally simpler model which postulates a log-linear relationship between the mortality rate and time is considered. To provide a fair comparison between this model and the log-bilinear model, we carry out rigorous investigations on the prior specifications to ensure consistency in terms of the prior information postulated for the models. We propose to use Laplace prior distributions on the corresponding parameters for the loglinear model. Finally, we demonstrate that the inclusion of cohort components is crucial to yield more accurate projections and to avoid unnecessarily wide prediction intervals by improving the calibration between data signals and errors.

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Bayesian Estimation and Model Comparison for Mortality Forecasting - Version of Record
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Published date: 2017

Identifiers

Local EPrints ID: 415627
URI: http://eprints.soton.ac.uk/id/eprint/415627
PURE UUID: cb7d9748-6bdd-4d2b-a464-3726c3d8095f
ORCID for Jonathan Forster: ORCID iD orcid.org/0000-0002-7867-3411
ORCID for Peter W F Smith: ORCID iD orcid.org/0000-0003-4423-5410

Catalogue record

Date deposited: 16 Nov 2017 17:30
Last modified: 12 Dec 2021 02:48

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Contributors

Author: Jackie Siaw Tze Wong
Thesis advisor: Jonathan Forster ORCID iD
Thesis advisor: Peter W F Smith ORCID iD

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