RT journal article
T1 Deriving Robust Bayesian Premiums under Bands of Prior Distributions with Applications
A1 Sánchez Sánchez, Marta
A1 Sordo Díaz, Miguel Ángel
A1 Suárez Llorens, Alfonso
A1 Gómez-Déniz, Emilio
A2 Estadística e Investigación Operativa
K1 Credibility
K1 class of priors
K1 distortion functions
K1 Kolmogorov and Kantorovich metrics
K1 premium calculation principle
K1 robust Bayesian analysis
K1 stochastic orders
AB We study the propagation of uncertainty from a class of priors introduced byArias-Nicolás et al. [(2016) Bayesian Analysis, 11(4), 1107–1136] to the premiums(both the collective and the Bayesian), for a wide family of premiumprinciples (specifically, those that preserve the likelihood ratio order). Theclass under study reflects the prior uncertainty using distortion functions andfulfills some desirable requirements: elicitation is easy, the prior uncertaintycan be measured by different metrics, and the range of quantities of interestis easily obtained from the extremal members of the class. We illustrate themethodology with several examples based on different claim counts models.
PB Cambridge Unversity Press
SN 0515-0361
YR 2019
FD 2019
LK http://hdl.handle.net/10498/29875
UL http://hdl.handle.net/10498/29875
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026