On a Prior Based on the Wasserstein Information Matrix

Author(s)

Wuchen Li, University of South CarolinaFollow
F. J. Rubio

Document Type

Article

Abstract

We introduce a prior for the parameters of univariate continuous distributions, based on the Wasserstein information matrix, which is invariant under reparameterisations. We discuss the links between the proposed prior with information geometry. We present sufficient conditions for the propriety of the posterior distribution for general classes of models. We present a simulation study that shows that the induced posteriors have good frequentist properties.

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.spl.2022.109645

Publication Info

Published in Statistics & Probability Letters, Volume 190, 2022.

APA Citation

Li, W., & Rubio, F. J. (2022). On a prior based on the Wasserstein information matrix. Statistics & Probability Letters, 190, 109645. https://doi.org/10.1016/j.spl.2022.109645

Rights

© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).