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Gaussian proces models for mortality rates and improvement factors

Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20180032059
003  MAP
005  20220911211517.0
008  181119e20180903gbr|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20180014666‎$a‎Ludkovski, Mike
24510‎$a‎Gaussian proces models for mortality rates and improvement factors‎$c‎Mike Ludkovski, Jimmy Risk, Howard Zail
520  ‎$a‎We develop a Gaussian process (GP) framework for modeling mortality rates and mortality improvement factors. GP regression is a nonparametric, data driven approach for determining the spatial dependence in mortality rates and jointly smoothing raw rates across dimensions, such as calendar year and age. The GP model quantifies uncertainty associated with smoothed historical experience and generates full stochastic trajectories for out-of-sample forecasts.
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
650 4‎$0‎MAPA20080592059‎$a‎Modelos predictivos
650 4‎$0‎MAPA20080555306‎$a‎Mortalidad
650  ‎$0‎MAPA20220007825‎$a‎Data driven
7001 ‎$0‎MAPA20180014673‎$a‎Risk, Jimmy
7001 ‎$0‎MAPA20180014680‎$a‎Zail, Howard
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎03/09/2018 Volumen 48 Número 3 - septiembre 2018 , p. 1307-1347