Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors

Liang (Jason) Hong
University of Texas at Dallas
Friday, March 19, 2021 - 12:00pm to 1:00pm
Zoom: https://umn.zoom.us/j/94564033758

In the context of predicting future claims, a fully Bayesian analysis – one that
specifies a statistical model, prior distribution, and updates using Bayes’s formula – is often viewed as the gold-standard, while Bühlmann’s credibility estimator serves as a simple approximation. But those desirable properties that give the Bayesian solution its elevated status depend critically on the posited model being correctly specified. Here we investigate the asymptotic behavior of Bayesian posterior distributions under a misspecified model, and our conclusion is that misspecification bias generally has damaging effects
that can lead to inaccurate inference and prediction. The credibility estimator, on the other hand, is not sensitive at all to model misspecification, giving it an advantage over the Bayesian solution in those practically relevant cases where the model is uncertain. This begs the question: does robustness to model misspecification require that we abandon uncertainty quantification based on a posterior distribution? Our answer to this question is No, and we offer an alternative Gibbs posterior construction. Furthermore, we argue that this Gibbs perspective provides a new characterization of Bühlmann’s credibility estimator.

Bio: Liang Hong, PhD, FSA, is an Associate Professor in the Department of Mathematical Sciences at the University of Texas at Dallas. His current research interests are actuarial science and foundations of mathematics. In actuarial science, he is primarily interested in applying machine/statistical learning methods, such as Bayesian non-parametric models, conformal prediction, and Gibbs posteriors, to solve important insurance problems.