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dc.contributor.authorDette, Holger-
dc.contributor.authorKokot, Kevin-
dc.date.accessioned2020-04-30T15:22:06Z-
dc.date.available2020-04-30T15:22:06Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/2003/39097-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-21015-
dc.description.abstractWe study the problem of testing the equivalence of functional parameters (such as the mean or variance function) in the two sample functional data problem. In contrast to previous work, which reduces the functional problem to a multiple testing problem for the equivalence of scalar data by comparing the functions at each point, our approach is based on an estimate of a distance measuring the maximum deviation between the two functional parameters. Equivalence is claimed if the estimate for the maximum deviation does not exceed a given threshold. A bootstrap procedure is proposed to obtain quantiles for the distribution of the test statistic and consistency of the corresponding test is proved in the large sample scenario. As the methods proposed here avoid the use of the intersectionunion principle they are less conservative and more powerful than the currently available methodology.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;11/2020-
dc.subjectequivalence testsen
dc.subjectBanach space valued random variablesen
dc.subjectmaximum deviationen
dc.subjectbootstrapen
dc.subjecttwo sample problemsen
dc.subjectfunctional dataen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleEfficient tests for bio-equivalence in functional dataen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dc.subject.rswkFunktionale Datenanalysede
dcterms.accessRightsopen access-
eldorado.secondarypublicationfalsede
Appears in Collections:Sonderforschungsbereich (SFB) 823

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