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Parsimonious parameterization of age-period-cohort models by Bayesian Shrinkage

Recurso electrónico / electronic resource
MARC record
Tag12Value
LDR  00000cab a2200000 4500
001  MAP20180005602
003  MAP
005  20180320110250.0
008  180226e20180101bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20180001970‎$a‎Venter, Gary
24510‎$a‎Parsimonious parameterization of age-period-cohort models by Bayesian Shrinkage‎$c‎Gary Venter, Sule Sahín
520  ‎$a‎Age-period-cohort models used in life and general insurance can be overparameterized, and actuaries have used several methods to avoid this, such as cubic splines. Regularization is a statistical approach for avoiding overparameterization, and it can reduce estimation and predictive variances compared to MLE. In Markov Chain Monte Carlo (MCMC) estimation, regularization is accomplished by the use of mean-zero priors, and the degree of parsimony can be optimized by numerically efficient out-of-sample cross-validation. This provides a consistent framework for comparing a variety of regularized MCMC models, such as those built with cubic splines, linear splines (as ours is), and the limiting case of non-regularized estimation. We apply this to the multiple-trend model of Hunt and Blake (2014).
650 4‎$0‎MAPA20080592042‎$a‎Modelos matemáticos
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
650 4‎$0‎MAPA20100065242‎$a‎Teorema de Bayes
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
7001 ‎$0‎MAPA20160009699‎$a‎Sahin, Sule
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎01/01/2018 Volumen 48 Número 1 - enero 2018 , p. 89-110