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Logistic regression for insured mortality experience studies

Recurso electrónico / Electronic Resource
Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20160004946
003  MAP
005  20160226143848.0
008  160218e20151201esp|||p |0|||b|spa d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎341
24500‎$a‎Logistic regression for insured mortality experience studies‎$c‎Zhiwei Zhu...[et al.]
520  ‎$a‎Properly adapted statistical modeling methodology can be a powerful tool for coping with a broad range of challenges related to life and annuity insurance industries' experience studies. In this article, we present a logistic regression model based on U.S. insured mortality experience study with a focus on gaining study efficiency and effectiveness by addressing multiple analytical predicaments within one statistical modeling framework. These predicaments include but are not limited to testing statistical significances or credibility of potential mortality drivers, estimation of normalized mortality, slopes, and differentials, quantification of study reliability, and extrapolation for under-experienced mortality, smoothing between select and ultimate estimations, and development of basic experience tables.
650 4‎$0‎MAPA20080597733‎$a‎Modelos estadísticos
650 4‎$0‎MAPA20080570590‎$a‎Seguro de vida
650 4‎$0‎MAPA20080555306‎$a‎Mortalidad
650 4‎$0‎MAPA20080601508‎$a‎Estudios estadísticos
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
651 1‎$0‎MAPA20080638337‎$a‎Estados Unidos
7001 ‎$0‎MAPA20160002720‎$a‎Zhu, Zhiwei
7730 ‎$w‎MAP20077000239‎$t‎North American actuarial journal‎$d‎Schaumburg : Society of Actuaries, 1997-‎$x‎1092-0277‎$g‎01/12/2015 Tomo 19 Número 4 - 2015 , p. 241-255