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dc.contributor.authorAchab, Massil
dc.contributor.authorBacry, Emmanuel
dc.contributor.authorGaïffas, Stéphane
dc.contributor.authorMastromatteo, Iacopo
dc.contributor.authorMuzy, Jean-François
dc.date.accessioned2019-12-16T12:47:08Z
dc.date.available2019-12-16T12:47:08Z
dc.date.issued2017
dc.identifier.issn1532-4435
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20324
dc.language.isoenen
dc.subjectHawkes Processen
dc.subjectCausality Inferenceen
dc.subjectCumulantsen
dc.subjectGeneralized Method of Momentsen
dc.subject.ddc519en
dc.titleUncovering Causality from Multivariate Hawkes Integrated Cumulantsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each node of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an estimation of this matrix without any parametric modeling and estimation of the kernels themselves. As a consequence, it can give an estimation of causality relationships between nodes (or users), based on their activity timestamps (on a social network for instance), without knowing or estimating the shape of the activities lifetime. For that purpose, we introduce a moment matching method that fits the second-order and the third-order integrated cumulants of the process. A theoretical analysis allows us to prove that this new estimation technique is consistent. Moreover, we show, on numerical experiments, that our approach is indeed very robust with respect to the shape of the kernels and gives appealing results on the MemeTracker database and on financial order book data.en
dc.relation.isversionofjnlnameJournal of Machine Learning Research
dc.relation.isversionofjnlvol18en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2017-01
dc.relation.isversionofjnlpages6998-7025en
dc.relation.isversionofjnlpublisherMIT Pressen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershipnon-rechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2019-12-16T12:41:52Z
hal.person.labIds89626
hal.person.labIds17$$$60
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