A likelihood ratio approach to sequential change point detection

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dc.contributor.authorDette, Holger
dc.contributor.authorGösmann, Josua
dc.date.accessioned2018-02-28T12:58:43Z
dc.date.available2018-02-28T12:58:43Z
dc.date.issued2018
dc.description.abstractIn this paper we propose a new approach for sequential monitoring of a parameter of a d-dimensional time series. We consider a closed-end-method, which is motivated by the likelihood ratio test principle and compare the new method with two alternative procedures. We also incorporate self-normalization such that estimation of the longrun variance is not necessary. We prove that for a large class of testing problems the new detection scheme has asymptotic level a and is consistent. The asymptotic theory is illustrated for the important cases of monitoring a change in the mean, variance and correlation. By means of a simulation study it is demonstrated that the new test performs better than the currently available procedures for these problems.en
dc.identifier.urihttp://hdl.handle.net/2003/36782
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-18783
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;2/2018en
dc.subjectchange point analysisen
dc.subjectlikelihood ratio principleen
dc.subjectsequential monitoringen
dc.subjectself-normalizationen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkChange-point-Problemde
dc.subject.rswkSequenzieller Testde
dc.subject.rswkLikelihood-Quotienten-Testde
dc.titleA likelihood ratio approach to sequential change point detectionen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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