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Gaussian process regression method for forecasting of mortality rates

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journal contribution
posted on 15.01.2019, 14:44 by Bo Wang, Ruhao Wu
Gaussian process regression (GPR) has long been shown to be a powerful and effective Bayesian nonparametric approach, and has been applied to a wide range of fields. In this paper we present a new application of Gaussian process regression methods for the modelling and forecasting of human mortality rates. The age-specific mortality rates are treated as time series and are modelled by four conventional Gaussian process regression models. Furthermore, to improve the forecasting accuracy we propose to use a weighted mean function and the spectral mixture covariance function in the GPR model. The numerical experiments show that the combination of the weighted mean function and the spectral mixture covariance function provides the best performance in forecasting long term mortality rates. The performance of the proposed method is also compared with three existing models in the mortality modelling literature, and the results demonstrate that the GPR model with the weighted mean function and the spectral mixture covariance function provides a more robust forecast performance.

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Citation

Neurocomputing, 2018, 316, pp. 232-239

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Mathematics

Version

AM (Accepted Manuscript)

Published in

Neurocomputing

Publisher

Elsevier

issn

0925-2312

Acceptance date

01/08/2018

Copyright date

2018

Available date

07/08/2019

Publisher version

https://www.sciencedirect.com/science/article/pii/S092523121830907X

Notes

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. Following the embargo period the above license applies. The full text may be available through the publisher links provided above.

Language

en

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