Wavelet-based feature extraction for mortality projection

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<dc:creator>Hainaut, Donatien</dc:creator>
<dc:creator>Denuit, Michel</dc:creator>
<dc:date>2020-09-01</dc:date>
<dc:description xml:lang="es">Sumario: Wavelet 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.</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/173039.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Modelos matemáticos</dc:subject>
<dc:subject xml:lang="es">Mortalidad</dc:subject>
<dc:subject xml:lang="es">Predicciones estadísticas</dc:subject>
<dc:subject xml:lang="es">Modelos actuariales</dc:subject>
<dc:subject xml:lang="es">Distribución Poisson-Beta</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Wavelet-based feature extraction for mortality projection</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 01/09/2020 Volumen 50 Número 3 - septiembre 2020 , p. 675-707</dc:relation>
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