Limiting the Shrinkage for the Exceptional by Objective Robust Bayesian Analysis: the `Clemente Problem'

TL;DR

This paper introduces robust Bayesian methods with heavy-tailed priors to effectively handle exceptional cases, outperforming traditional estimators like James-Stein in terms of mean squared error.

Contribution

It proposes a novel robust Bayesian framework using heavy-tailed priors to address the Clemente problem, improving estimation accuracy for exceptional cases.

Findings

Robust priors reduce errors in exceptional cases.

Empirical Bayes and Full Bayes approaches yield similar results.

Robust methods outperform James-Stein estimator in experiments.

Abstract

In `borrowing strength' an important problem of Statistics is to treat exceptional cases in a fundamentally different. This is what has been coined as `the Clemente problem' in honor of R. Clemente (Efron 2010). In this article, we propose to use robust penalties, in the form of losses that penalize more severely huge errors, or (equivalently) priors of heavy tails which make more probable the exceptional. Using heavy tailed priors, we can reproduce in a Bayesian way Efron and Morris `limited translated estimators' (with Double Exponential Priors) and `discarding priors estimators' (with Cauchy like priors), which discard the prior in the presence of conflict. Both Empirical Bayes and Full Bayes approaches are able to alleviate the Clemente Problem and furthermore beat the James-Stein estimator in terms of smaller square errors, for sensible Robust Bayes priors. We model in parallel Empirical Bayes and Fully Bayesian hierarchical models, illustrating that the differences among sensible versions of both are relatively small, as compared with the effect due to the robust assumptions.