Séminaire de mathématiques appliquées

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Nom de l'orateur
Cathy Maugis-Rabusseau
Etablissement de l'orateur
INSA Toulouse
Lieu de l'exposé
Salle des séminaires
Date et heure de l'exposé

Classical inference methods fail when applied to data-driven test hypotheses. Selective inference is particularly relevant post-clustering, typically when testing a difference in the mean between two clusters. Thus, dedicated methodologies are required to obtain statistical quarantees for these selective inference problems. In this work, we address convex clustering with l1 penalization, by leveraging related selective inference tools for regression, based on Gaussian vectors conditioned to polyhedral sets.