Multiscale change point detection for dependent data

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dc.contributor.authorDette, Holger
dc.contributor.authorSchüler, Theresa
dc.contributor.authorVetter, Mathias
dc.date.accessioned2018-11-16T13:21:21Z
dc.date.available2018-11-16T13:21:21Z
dc.date.issued2018
dc.description.abstractIn this paper we study the theoretical properties of the simultaneous multiscale change point estimator (SMUCE) proposed by Frick et al. (2014) in regression models with dependent error processes. Empirical studies show that in this case the change point estimate is inconsistent, but it is not known if alternatives suggested in the literature for correlated data are consistent. We propose a modification of SMUCE scaling the basic statistic by the long run variance of the error process, which is estimated by a difference-type variance estimator calculated from local means from different blocks. For this modification we prove model consistency for physical dependent error processes and illustrate the finite sample performance by means of a simulation study.en
dc.identifier.urihttp://hdl.handle.net/2003/37806
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-19801
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;28/2018
dc.subjectchange point detectionen
dc.subjectphysical dependent processesen
dc.subjectmultiscale methodsen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkChange-point-Problemde
dc.subject.rswkRegressionsanalysede
dc.subject.rswkNichtparametrisches Verfahrende
dc.titleMultiscale change point detection for dependent dataen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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