EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH
In this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed...
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| Vydané v: | ASTIN Bulletin : The Journal of the IAA Ročník 52; číslo 3; s. 789 - 812 |
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| Jazyk: | English |
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New York, USA
Cambridge University Press
01.09.2022
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| ISSN: | 0515-0361, 1783-1350 |
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| Abstract | In this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods. |
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| AbstractList | In this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods. |
| Author | Miyata, Akihiro Matsuyama, Naoki |
| Author_xml | – sequence: 1 givenname: Akihiro surname: Miyata fullname: Miyata, Akihiro organization: Graduate Student at AMS Meiji University Tokyo, Japan – sequence: 2 givenname: Naoki surname: Matsuyama fullname: Matsuyama, Naoki email: ma2yama@meiji.ac.jp organization: Graduate School of Advanced Mathematical Sciences (AMS) Meiji University Tokyo, Japan E-mail: ma2yama@meiji.ac.jp |
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| Cites_doi | 10.1017/asb.2021.13 10.3390/risks7010033 10.1017/S1748499519000071 10.1017/asb.2021.34 10.1017/S1748499517000069 10.1093/biostatistics/kxj024 10.1080/10920277.2009.10597538 10.1080/03461238.2020.1867232 10.1007/BF02551274 10.2139/ssrn.3822407 10.1017/asb.2017.45 |
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| Copyright | The Author(s), 2022. Published by Cambridge University Press on behalf of The International Actuarial Association The Author(s), 2022. Published by Cambridge University Press on behalf of The International Actuarial Association. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Title | EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH |
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