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DERIVING ROBUST BAYESIAN PREMIUMS UNDER BANDS OF PRIOR DISTRIBUTIONS WITH APPLICATIONS

Published online by Cambridge University Press:  23 November 2018

M. Sánchez-Sánchez
Affiliation:
Departamento de Estadística e Investigación Operativa, Facultad de Ciencias, Universidad de Cádiz, Avda. Rep. Saharaui s/n, 11510 Puerto Real, Cádiz, Spain E-Mail: [email protected]
M.A. Sordo
Affiliation:
Departamento de Estadística e Investigación Operativa, Facultad de Ciencias, Universidad de Cádiz, Avda. Rep. Saharaui s/n, 11510 Puerto Real, Cádiz, Spain E-Mail: [email protected]
A. Suárez-Llorens*
Affiliation:
Departamento de Estadística e Investigación Operativa, Facultad de Ciencias, Universidad de Cádiz, Avda. Rep. Saharaui s/n, 11510 Puerto Real, Cádiz, Spain E-Mail: [email protected]
E. Gómez-Déniz
Affiliation:
Department of Quantitative MethodsUniversity of Las Palmas de Gran CanariaCampus Universitario de Tafira 35017 las Palmas de Gran Canaria, Spain E-Mail: [email protected]

Abstract

We study the propagation of uncertainty from a class of priors introduced by Arias-Nicolás et al. [(2016) Bayesian Analysis, 11(4), 1107–1136] to the premiums (both the collective and the Bayesian), for a wide family of premium principles (specifically, those that preserve the likelihood ratio order). The class under study reflects the prior uncertainty using distortion functions and fulfills some desirable requirements: elicitation is easy, the prior uncertainty can be measured by different metrics, and the range of quantities of interest is easily obtained from the extremal members of the class. We illustrate the methodology with several examples based on different claim counts models.

Type
Research Article
Copyright
Copyright © Astin Bulletin 2018 

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