Compound decisions and empirical Bayes via Bayesian nonparametrics

TL;DR

This paper investigates Bayesian nonparametric methods for the Gaussian sequence compound decision problem, demonstrating near-optimal regret bounds and comparing Bayesian procedures with classical estimators.

Contribution

It introduces a Dirichlet-process-based Bayesian estimator that achieves near-optimal regret bounds and compares its performance to the nonparametric maximum likelihood estimator.

Findings

Bayesian nonparametric estimator attains near-optimal regret bounds.

Posterior mean Bayes rule is admissible, unlike the NPMLE plug-in rule.

Results hold under both fixed mean vector and hierarchical models.

Abstract

We study the Gaussian sequence compound decision problem and analyze a Bayesian nonparametric estimator from an empirical Bayes, regret-based perspective. Motivated by sharp results for the classical nonparametric maximum likelihood estimator (NPMLE), we ask whether an analogous guarantee can be obtained using a standard Bayesian nonparametric prior. We show that a Dirichlet-process-based Bayesian procedure achieves near-optimal regret bounds. Our main results are stated in the compound decision framework, where the mean vector is treated as fixed, while we also provide parallel guarantees under a hierarchical model in which the means are drawn from a true unknown prior distribution. The posterior mean Bayes rule is, a fortiori, admissible, whereas we show that the NPMLE plug-in rule is inadmissible.