RT journal article
T1 Measuring Bayesian sensitivity in the compound Poisson process
A1 Ruggeri, F.
A1 Sánchez Sánchez, Marta
A1 Suárez Llorens, Alfonso
A2 Estadística e Investigación Operativa
K1 Robust Bayesian analysis
K1 Class of priors
K1 Weighted densities
K1 Premium calculation principle
K1 Stochastic orders
K1 Bonus–Malus system
AB Bayesian methods are widely used to determine insurance premiums, though they are sometimes criticized for the arbitrariness in selecting prior distributions. To mitigate this issue, classes of priors incorporating expert knowledge have been proposed, allowing for the analysis of uncertainty through upper and lower bounds on Bayesian premiums. In this paper, we employ a recently introduced class of priors based on stochastic orders, where the induced order on prior distributions is preserved in the corresponding posterior distributions. Uncertainty around a prior is captured through weighted functions, and the extremal elements of the class define premium bounds. We also show how dependence among parameters can be integrated using suitable weight functions. Our approach is developed within the framework of the compound Poisson process, a fundamental model for claim frequency and severity in car insurance. Additionally, we present a sensitivity analysis method for a bonus–malus system (BMS).
PB Springer
SN 1863-8260
YR 2025
FD 2025
LK http://hdl.handle.net/10498/39169
UL http://hdl.handle.net/10498/39169
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026