Agenda

Stefano Favaro (Università di Torino) - Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem (Joint work with Sandra Fortini, Bocconi University)

Abstract:

The Poisson compound decision problem is a long-standing problem in statistics, where empirical Bayes methodologies are commonly used to estimate Poisson's means in static or batch domains. In this paper, we study the Poisson compound decision problem in a streaming or online domain. Adopting a quasi-Bayesian approach, often referred to as Newton’s algorithm, we obtain a sequential Poisson's mean estimate that is easy to evaluate, computationally efficient, and maintain a constant per-observation computational cost as data accumulate. Asymptotic frequentist guarantees of this estimate are established, showing consistency and asymptotic optimality, where the latter is understood as vanishing excess Bayes risk or regret. We demonstrate the effectiveness of our methodology through empirical analysis on synthetic and real data, with comparisons to existing parametric and nonparametric approaches.

Zoom link: https://unive.zoom.us/j/88317242811?pwd=xafdhybiUXJTU4EtbgavlrSEJXyb9K.1
Meeting ID: 883 1724 2811
Passcode: j5jW3b