Regularized estimation of Monge-Kantorovich quantiles for spherical data
Abstract
Tools from optimal transport (OT) theory have recently been used to define a notion of quantile function for directional data. In practice, regularization is mandatory for applications that require out-of-sample estimates. To this end, we introduce a regularized estimator built from entropic optimal transport, by extending the definition of the entropic map to the spherical setting. We propose a stochastic algorithm to directly solve a continuous OT problem between the uniform distribution and a target distribution, by expanding Kantorovich potentials in the basis of spherical harmonics. In addition, we define the directional Monge-Kantorovich depth, a companion concept for OT-based quantiles. We show that it benefits from desirable properties related to Liu-Zuo-Serfling axioms for the statistical analysis of directional data. Building on our regularized estimators, we illustrate the benefits of our methodology for data analysis.
Cite
@article{arxiv.2407.02085,
title = {Regularized estimation of Monge-Kantorovich quantiles for spherical data},
author = {Bernard Bercu and Jérémie Bigot and Gauthier Thurin},
journal= {arXiv preprint arXiv:2407.02085},
year = {2026}
}