Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/49374 
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dc.contributor.authorSteland, Ansgaren
dc.date.accessioned2011-09-06T11:45:37Z-
dc.date.available2011-09-06T11:45:37Z-
dc.date.issued2003-
dc.identifier.urihttp://hdl.handle.net/10419/49374-
dc.description.abstractIn many applications one is interested to detect certain (known) patterns in the mean of a process with smallest delay. Using an asymptotic framework which allows to capture that feature, we study a class of appropriate sequential nonparametric kernel procedures under local nonparametric alternatives. We prove a new theorem on the convergence of the normed delay of the associated sequential detection procedure which holds for dependent time series under a weak mixing condition. The result suggests a simple procedure to select a kernel from a finite set of candidate kernels, and therefore may also be of interest from a practical point of view. Further, we provide two new theorems about the existence and an explicit representation of optimal kernels minimizing the asymptotic normed delay. The results are illustrated by some examples.en
dc.language.isoengen
dc.publisher|aUniversität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen |cDortmunden
dc.relation.ispartofseries|aTechnical Report |x2003,27en
dc.subject.ddc519en
dc.subject.keywordEnzyme kineticsen
dc.subject.keywordfinancial econometricsen
dc.subject.keywordnonparametric regressionen
dc.subject.keywordstatistical geneticsen
dc.subject.keywordquality controlen
dc.subject.stwSchätztheorieen
dc.subject.stwStatistischer Testen
dc.subject.stwNichtparametrisches Verfahrenen
dc.subject.stwFinanzmarkten
dc.subject.stwTheorieen
dc.subject.stwSequentialanalyseen
dc.titleOptimal sequential kernel detection for dependent processes-
dc.typeWorking Paperen
dc.identifier.ppn823219860en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:sfb475:200327en

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