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dc.contributor.authorKashlak, AB
dc.contributor.authorAston, John
dc.contributor.authorNickl, Richard
dc.date.accessioned2018-10-03T04:46:03Z
dc.date.available2018-10-03T04:46:03Z
dc.date.issued2019-02
dc.identifier.issn0972-7671
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/283115
dc.description.abstractWe propose a novel approach to the analysis of covariance operators making use of concentration inequalities. First, non-asymptotic confidence sets are constructed for such operators. Then, subsequent applications including a k sample test for equality of covariance, a functional data classifier, and an expectation-maximization style clustering algorithm are derived and tested on both simulated and phoneme data.
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleInference on covariance operators via concentration inequalities: K-sample tests, classification, and clustering via rademacher complexities
dc.typeArticle
prism.endingPage243
prism.publicationDate2019
prism.publicationNameSankhya: The Indian Journal of Statistics
prism.startingPage214
prism.volume81A
dc.identifier.doi10.17863/CAM.30476
rioxxterms.versionofrecord10.1007/s13171-018-0143-9
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-01-01
dc.identifier.eissn0976-8378
dc.publisher.urlhttp://dx.doi.org/10.1007/s13171-018-0143-9
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K021672/2)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L016516/1)
cam.issuedOnline2018-09-28


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International