Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
Authors
Research Topics
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asag037 -
Published:
June 18, 2026 -
Added to Tracker:
Jun 19, 2026
Abstract
Summary The Poisson compound decision problem is a long-standing problem is statistics, for which empirical Bayes methods are commonly used to estimate Poisson means in static or batch settings. We consider this problem in a streaming, or online, framework. Building on a quasi-Bayesian approach based on Newton’s algorithm, we develop a sequential estimate that is easy to evaluate, computationally efficient, and has constant per-observation cost as the data accrue. We establish frequentist guarantees for the proposed estimate, including consistency and asymptotic optimality, with optimality understood as vanishing excess Bayes risk, or regret. Empirical performance is assessed through simulation studies and comparisons with benchmark procedures.
Author Details
S Favaro
AuthorS Fortini
AuthorResearch Topics & Keywords
Bayesian Statistics
Research AreaCitation Information
APA Format
S Favaro
&
S Fortini
(2026)
.
Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem.
Biometrika
, 10.1093/biomet/asag037.
BibTeX Format
@article{paper1294,
title = { Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem },
author = {
S Favaro
and S Fortini
},
journal = { Biometrika },
year = { 2026 },
doi = { 10.1093/biomet/asag037 },
url = { https://doi.org/10.1093/biomet/asag037 }
}