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A Flexible bayesian nonparametric model for predicting future insurance claims

Recurso electrónico / Electronic resource
MARC record
Tag12Value
LDR  00000cab a2200000 4500
001  MAP20170033226
003  MAP
005  20171122122213.0
008  171017e20170605esp|||p |0|||b|spa d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20150005984‎$a‎Hong, Liang
24512‎$a‎A Flexible bayesian nonparametric model for predicting future insurance claims‎$c‎Liang Hong, Ryan Martín
520  ‎$a‎Accurate prediction of future claims is a fundamentally important problem in insurance. The Bayesian approach is natural in this context, as it provides a complete predictive distribution for future claims. The classical credibility theory provides a simple approximation to the mean of that predictive distribution as a point predictor, but this approach ignores other features of the predictive distribution, such as spread, that would be useful for decision making. In this article, we propose a Dirichlet process mixture of log-normals model and discuss the theoretical properties and computation of the corresponding predictive distribution. Numerical examples demonstrate the benefit of our model compared to some existing insurance loss models, and an R code implementation of the proposed method is also provided.
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20100065242‎$a‎Teorema de Bayes
7001 ‎$0‎MAPA20170014706‎$a‎Martín, Ryan
7730 ‎$w‎MAP20077000239‎$t‎North American actuarial journal‎$d‎Schaumburg : Society of Actuaries, 1997-‎$x‎1092-0277‎$g‎05/06/2017 Tomo 21 Número 2 - 2017 , p. 228-241