Imprecise Credibility Theory

19 Pages Posted: 20 Nov 2020

See all articles by Liang Hong

Liang Hong

The University of Texas at Dallas

Ryan Martin

North Carolina State University - Department of Statistics

Date Written: October 23, 2020

Abstract

The classical credibility theory is a cornerstone of experience rating especially in the field of property and casualty insurance. An obstacle to putting the credibility theory into practice is the conversion of available prior information into a precise choice of crucial hyperparameters. In most real-world applications, the information necessary to justify a precise choice is lacking, so we take a page out of the robust Bayesian book and propose an imprecise credibility estimator that honestly acknowledges the imprecision in the hyperparameter specification. This results in an interval estimator that is doubly-robust in the sense that it retains the credibility estimator's freedom from model specification and fast asymptotic concentration, while simultaneously being insensitive to prior hyperparameter specification.

Keywords: Imprecise probability; credibility estimator; model misspecification; prior uncertainty; robustness

Suggested Citation

Hong, Liang and Martin, Ryan, Imprecise Credibility Theory (October 23, 2020). Available at SSRN: https://ssrn.com/abstract=3717924 or http://dx.doi.org/10.2139/ssrn.3717924

Liang Hong (Contact Author)

The University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Ryan Martin

North Carolina State University - Department of Statistics ( email )

Raleigh, NC 27695-8203
United States

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