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Extending the lee-carter model with variational autoencoder: a fusion of neural network and bayesian approach

Extending the lee-carter model with variational autoencoder: a fusion of neural network and bayesian approach
Recurso electrónico / Electronic resource
Registro MARC
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003  MAP
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008  221004e20220905bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20220008563‎$a‎Miyata, Akihiro
24510‎$a‎Extending the lee-carter model with variational autoencoder: a fusion of neural network and bayesian approach‎$c‎Akihiro Miyata
520  ‎$a‎In this study, we propose a nonlinear Bayesian extension of the LeeCarter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods.
540  ‎$a‎La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY 4.0)"‎$f‎‎$u‎https://creativecommons.org/licenses/by/4.0‎$9‎43
650 4‎$0‎MAPA20100065273‎$a‎Modelo Lee-Carter
650 4‎$0‎MAPA20100065242‎$a‎Teorema de Bayes
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
7730 ‎$w‎MAP20077000420‎$g‎05/09/2022 Volumen 52 Número 3 - septiembre 2022 , p. 789-812‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association
856  ‎$q‎application/pdf‎$w‎1116870‎$y‎Recurso electrónico / Electronic resource