Likelihood distortion and Bayesian local robustness

TL;DR

This paper introduces a new, computationally tractable approach to assess the local robustness of Bayesian methods with respect to the likelihood function, extending to prior and joint robustness, using distortion functions.

Contribution

It proposes a novel local robustness measure for Bayesian analysis based on distortion functions, addressing the less-studied robustness w.r.t. likelihoods.

Findings

The robustness measure is easy to compute for various distortion functions.

Asymptotic properties of the measure are established.

Numerical experiments demonstrate the method's practical applicability.

Abstract

Robust Bayesian analysis has been mainly devoted to detecting and measuring robustness w.r.t. the prior distribution. Many contributions in the literature aim to define suitable classes of priors which allow the computation of variations of quantities of interest while the prior changes within those classes. The literature has devoted much less attention to the robustness of Bayesian methods w.r.t. the likelihood function due to mathematical and computational complexity, and because it is often arguably considered a more objective choice compared to the prior. In this contribution, we propose a new approach to Bayesian local robustness, mainly focusing on robustness w.r.t. the likelihood function. Successively, we extend it to account for robustness w.r.t. the prior, as well as the prior and the likelihood jointly. This approach is based on the notion of distortion function introduced in the literature on risk theory. The novel robustness measure is a local sensitivity measure that turns out to be very tractable and easy to compute for several classes of distortion functions. Asymptotic properties are derived, and numerical experiments illustrate the theory and its applicability for modelling purposes.