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Compound decisions and empirical Bayes via Bayesian nonparametrics

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  • Nikolaos Ignatiadis
  • Sid Kankanala

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.

Suggested Citation

  • Nikolaos Ignatiadis & Sid Kankanala, 2026. "Compound decisions and empirical Bayes via Bayesian nonparametrics," Papers 2602.20115, arXiv.org.
    • Handle: RePEc:arx:papers:2602.20115
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    File URL: http://arxiv.org/pdf/2602.20115
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