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hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBIGOT, Jérémie
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCAZELLES, Elsa
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
dc.date.accessioned2024-04-04T03:02:11Z
dc.date.available2024-04-04T03:02:11Z
dc.date.issued2019
dc.identifier.issn1935-7524
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192955
dc.description.abstractEnThe notion of entropy-regularized optimal transport, also known as Sinkhorn divergence, has recently gained popularity in machine learning and statistics, as it makes feasible the use of smoothed optimal transportation distances for data analysis. The Sinkhorn divergence allows the fast computation of an entropically regularized Wasserstein distance between two probability distributions supported on a finite metric space of (possibly) high-dimension. For data sampled from one or two unknown probability distributions, we derive the distributional limits of the empirical Sinkhorn divergence and its centered version (Sinkhorn loss). We also propose a bootstrap procedure which allows to obtain new test statistics for measuring the discrepancies between multivariate probability distributions. Our work is inspired by the results of Sommerfeld and Munk (2016) on the asymptotic distribution of empirical Wasserstein distance on finite space using unregularized transportation costs. Incidentally we also analyze the asymptotic distribution of entropy-regularized Wasserstein distances when the regularization parameter tends to zero. Simulated and real datasets are used to illustrate our approach.
dc.description.sponsorshipGeneralized Optimal Transport Models for Image processing - ANR-16-CE33-0010
dc.language.isoen
dc.publisherShaker Heights, OH : Institute of Mathematical Statistics
dc.title.enCentral limit theorems for entropy-regularized optimal transport on finite spaces and statistical applications
dc.typeArticle de revue
dc.subject.halMathématiques [math]/Statistiques [math.ST]
bordeaux.journalElectronic Journal of Statistics
bordeaux.page5120-5150
bordeaux.volume13
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01647869
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01647869v1
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