Robust Elicitable Functionals

TL;DR

This paper introduces a robust framework for elicitable functionals that accounts for small distribution misspecifications using Kullback-Leibler divergence, ensuring unique solutions and applicability in reinsurance and robust regression.

Contribution

It develops a robustified version of elicitable functionals with conditions for existence and uniqueness, and introduces b-homogeneous scoring functions to preserve statistical properties.

Findings

Robust elicitable functionals admit unique solutions at the boundary of uncertainty.

The proposed framework applies to reinsurance and robust regression scenarios.

B-homogeneous scoring functions maintain desirable properties under robustness.

Abstract

Elicitable functionals and (strictly) consistent scoring functions are of interest due to their utility of determining (uniquely) optimal forecasts, and thus the ability to effectively backtest predictions. However, in practice, assuming that a distribution is correctly specified is too strong a belief to reliably hold. To remediate this, we incorporate a notion of statistical robustness into the framework of elicitable functionals, meaning that our robust functional accounts for "small" misspecifications of a baseline distribution. Specifically, we propose a robustified version of elicitable functionals by using the Kullback-Leibler divergence to quantify potential misspecifications from a baseline distribution. We show that the robust elicitable functionals admit unique solutions lying at the boundary of the uncertainty region, and provide conditions for existence and uniqueness. Since every elicitable functional possesses infinitely many scoring functions, we propose the class of b-homogeneous strictly consistent scoring functions, for which the robust functionals maintain desirable statistical properties. We show the applicability of the robust elicitable functional in several examples: in a reinsurance setting and in robust regression problems.