Work

On the optimality of coin-betting for mean estimation

Year: 2025

Type: article

Abstract: We consider the problem of testing the mean of a bounded real random variable. We introduce a notion of optimal classes for e-variables and e-processes, and establish the optimality of the coin-betting formulation among e-variable-based algorithmic frameworks for testing and estimating the (conditional) mean. As a consequence, we provide a direct and explicit characterisation of all valid e-variables and e-processes for this testing problem. In the language of classical statistical decision theory, we fully describe the set of all admissible e-variables and e-processes, and identify the corresponding minimal complete class.

Source: International Journal of Approximate Reasoning

DOI: 10.1016/j.ijar.2025.109550

Author: Eugenio Clerico

Institution: Universitat Pompeu Fabra

Language: English

Topic: Advanced Bandit Algorithms Research

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,050

Funders: Horizon 2020 Framework Programme, European Research Council, H2020 European Research Council