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Wavelet-based feature extraction for mortality projection

Recurso electrónico / Electronic resource
MARC record
Tag12Value
LDR  00000cab a2200000 4500
001  MAP20200029700
003  MAP
005  20200924174727.0
008  200924e20200901bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20180010453‎$a‎Hainaut, Donatien
24510‎$a‎Wavelet-based feature extraction for mortality projection‎$c‎Donatien Hainaut, Michel Denuit
520  ‎$a‎Wavelet theory is known to be a powerful tool for compressing and processing time series or images. It consists in projecting a signal on an orthonormal basis of functions that are chosen in order to provide a sparse representation of the data. The first part of this article focuses on smoothing mortality curves by wavelets shrinkage. A chi-square test and a penalized likelihood approach are applied to determine the optimal degree of smoothing. The second part of this article is devoted to mortality forecasting. Wavelet coefficients exhibit clear trends for the Belgian population from 1965 to 2015, they are easy to forecast resulting in predicted future mortality rates. The wavelet-based approach is then compared with some popular actuarial models of LeeCarter type estimated fitted to Belgian, UK, and US populations. The wavelet model outperforms all of them.
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20080592042‎$a‎Modelos matemáticos
650 4‎$0‎MAPA20080555306‎$a‎Mortalidad
650 4‎$0‎MAPA20120011137‎$a‎Predicciones estadísticas
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
650 4‎$0‎MAPA20090041721‎$a‎Distribución Poisson-Beta
7001 ‎$0‎MAPA20080096434‎$a‎Denuit, Michel
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎01/09/2020 Volumen 50 Número 3 - septiembre 2020 , p. 675-707