Deriving robust bayesian premiums under bands of prior distributions with applications
- A. Suarez Llorens
- M. Sánchez Sánchez
- M. A. Sordo Díaz
- E. Gómez Déniz
Editorial: Editado por Eva Vallada y Rubén Ruiz Grupo de Sistemas de Optimización Aplicada http://soa.iti.es/ Universitat Politècnica de València
ISBN: 978-84-09-13580-6
Año de publicación: 2019
Páginas: 165
Tipo: Aportación congreso
Resumen
We study the propagation of uncertainty from a class of priors introduced by AriasNicolas 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.