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dc.contributor.authorCarlier, Guillaume
dc.contributor.authorChernozhukov, Victor V
dc.contributor.authorGalichon, Alfred
dc.date.accessioned2019-11-01T18:03:33Z
dc.date.available2019-11-01T18:03:33Z
dc.date.issued2017-09
dc.identifier.issn0047-259X
dc.identifier.urihttps://hdl.handle.net/1721.1/122676
dc.description.abstractThis paper studies vector quantile regression (VQR), which models the dependence of a random vector with respect to a vector of explanatory variables with enough flexibility to capture the whole conditional distribution, and not only the conditional mean. The problem of vector quantile regression is formulated as an optimal transport problem subject to an additional mean-independence condition. This paper provides results on VQR beyond the specified case which had been the focus of previous work. We show that even beyond the specified case, the VQR problem still has a solution which provides a general representation of the conditional dependence between random vectors. Keywords: Duality; Optimal transport; Vector quantile regressionen_US
dc.language.isoen
dc.publisherElsevier BVen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.jmva.2017.07.003en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceElsevieren_US
dc.titleVector quantile regression beyond the specified caseen_US
dc.typeArticleen_US
dc.identifier.citationCarlier, Guillaume et al. "Vector quantile regression beyond the specified case." Journal of Multivariate Analysis, 161, (July 2017): 96-102 © 2017 The Authors.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Economicsen_US
dc.relation.journalJournal of Multivariate Analysisen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-10-21T18:13:08Z
dspace.date.submission2019-10-21T18:13:10Z
mit.journal.volume161en_US


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