LDR | | | 00000cab a2200000 4500 |
001 | | | MAP20220026123 |
003 | | | MAP |
005 | | | 20221004104208.0 |
008 | | | 221004e20220905bel|||p |0|||b|eng d |
040 | | | $aMAP$bspa$dMAP |
084 | | | $a6 |
100 | 1 | | $0MAPA20220008563$aMiyata, Akihiro |
245 | 1 | 0 | $aExtending the lee-carter model with variational autoencoder: a fusion of neural network and bayesian approach$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.
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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 |
773 | 0 | | $wMAP20077000420$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 |