A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Robust Bayesian insurance premium in a collective risk model with distorted priors under the generalised Bregman loss
2021
Statistics in Transition New Series
The article presents a collective risk model for the insurance claims. The objective is to estimate a premium, which is defined as a functional specified up to unknown parameters. For this purpose, the Bayesian methodology, which combines the prior knowledge about certain unknown parameters with the knowledge in the form of a random sample, has been adopted. The generalised Bregman loss function is considered. In effect, the results can be applied to numerous loss functions, including the
doi:10.21307/stattrans-2021-030
fatcat:b37ukuuegzax7dxtgouzkh3pku