Testing relevant hypotheses in functional time series via self-normalization

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dc.contributor.authorDette, Holger
dc.contributor.authorKokot, Kevin
dc.contributor.authorVolgushev, Stanislav
dc.date.accessioned2018-10-05T12:05:01Z
dc.date.available2018-10-05T12:05:01Z
dc.date.issued2018
dc.description.abstractIn this paper we develop methodology for testing relevant hypotheses in a tuning-free way. Our main focus is on functional time series, but extensions to other settings are also discussed. Instead of testing for exact equality, for example for the equality of two mean functions from two independent time series, we propose to test a relevant deviation under the null hypothesis. In the two sample problem this means that an L2-distance between the two mean functions is smaller than a pre-specified threshold. For such hypotheses self-normalization, which was introduced by Shao (2010) and Shao and Zhang (2010) and is commonly used to avoid the estimation of nuisance parameters, is not directly applicable. We develop new self-normalized procedures for testing relevant hypotheses in the one sample, two sample and change point problem and investigate their asymptotic properties. Finite sample properties of the proposed tests are illustrated by means of a simulation study and a data example.en
dc.identifier.urihttp://hdl.handle.net/2003/37138
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-19134
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;21/2018
dc.subjectself normalizationen
dc.subjectrelevant hypothesesen
dc.subjectCUSUMen
dc.subjectchange point analysisen
dc.subjecttwo sample problemsen
dc.subjectfunctional time seriesen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkHypothesentestde
dc.subject.rswkZeitreihede
dc.titleTesting relevant hypotheses in functional time series via self-normalizationen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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