2024/07/02 by Bernard Bercu, Bercu, Bernard, Jérémie Bigot +3
Economics, Econometrics and Finance · Social Sciences · #62G30 #62H11 #62L20 #Computation (stat.CO) #FOS: Computer and information sciences #Insurance, Mortality, Demography, Risk Management #Methodology (stat.ME) #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.2407.02085
openalex publication_date 2024/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.