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dc.contributor.authorCarpentier, Alexandra
dc.contributor.authorCollier, Olivier
dc.contributor.authorComminges, Laëtitia
dc.contributor.authorTsybakov, Alexandre
dc.contributor.authorWang, Yuhao
dc.date.accessioned2018-09-04T10:01:13Z
dc.date.available2018-09-04T10:01:13Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17937
dc.language.isoenen
dc.subjectsignal detectionen
dc.subjectsparsityen
dc.subjectGaussian sequence modelen
dc.subjectlinear regressionen
dc.subject.ddc519en
dc.titleMinimax rate of testing in sparse linear regressionen
dc.typeDocument de travail / Working paper
dc.description.abstractenWe consider the problem of testing the hypothesis that the parameter of linear regression model is 0 against an s-sparse alternative separated from 0 in the 2-distance. We show that, in Gaussian linear regression model with p < n, where p is the dimension of the parameter and n is the sample size, the non-asymptotic minimax rate of testing has the form sqrt((s/n) log(1 + sqrt(p)/s)). We also show that this is the minimax rate of estimation of the 2-norm of the regression parameter.en
dc.identifier.citationpages17en
dc.relation.ispartofseriestitleCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01770434en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.identifier.citationdate2018-04
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2018-09-04T09:54:57Z
hal.person.labIds4067
hal.person.labIds101
hal.person.labIds60
hal.person.labIds102$$$2579
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