On a prior based on the Wasserstein information matrix
Statistics Theory
2022-07-28 v3 Methodology
Statistics Theory
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.
Cite
@article{arxiv.2202.03217,
title = {On a prior based on the Wasserstein information matrix},
author = {W. Li and F. J. Rubio},
journal= {arXiv preprint arXiv:2202.03217},
year = {2022}
}
Comments
To appear in Statistics and Probability Letters