Statistical inference for high dimensional panel functional time series

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dc.contributor.authorZhou, Zhou
dc.contributor.authorDette, Holger
dc.date.accessioned2020-02-28T13:58:03Z
dc.date.available2020-02-28T13:58:03Z
dc.date.issued2020
dc.description.abstractIn this paper we develop statistical inference tools for high dimensional functional time series. We introduce a new concept of physical dependent processes in the space of square integrable functions, which adopts the idea of basis decomposition of functional data in these spaces, and derive Gaussian and multiplier bootstrap approximations for sums of high dimensional functional time series. These results have numerous important statistical consequences. Exemplarily, we consider the development of joint simultaneous confidence bands for the mean functions and the construction of tests for the hypotheses that the mean functions in the spatial dimension are parallel. The results are illustrated by means of a small simulation study and in the analysis of Canadian temperature data.en
dc.identifier.urihttp://hdl.handle.net/2003/39020
dc.identifier.urihttps://doi.org/10.17877/DE290R-20939
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;4/2020
dc.subjecthigh dimensional functional time seriesen
dc.subjectphysical dependenceen
dc.subjectGaussian approximationen
dc.subjectsimultaneous confidence bandsen
dc.subjecthypotheses testsen
dc.subjectspatio-temporal dataen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkZeitreihenanalysede
dc.subject.rswkGauß-Approximationde
dc.subject.rswkKonfidenzintervallde
dc.subject.rswkStatistischer Testde
dc.titleStatistical inference for high dimensional panel functional time seriesen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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