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dc.contributor.authorSteland, Ansgaren_US
dc.date.accessioned2011-09-06T11:45:37Z-
dc.date.available2011-09-06T11:45:37Z-
dc.date.issued2003en_US
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_US
dc.language.isoengen_US
dc.publisherUniv., SFB 475 Dortmunden_US
dc.relation.ispartofseriesTechnical Report // Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2003,27en_US
dc.subject.ddc330en_US
dc.subject.keywordEnzyme kineticsen_US
dc.subject.keywordfinancial econometricsen_US
dc.subject.keywordnonparametric regressionen_US
dc.subject.keywordstatistical geneticsen_US
dc.subject.keywordquality controlen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwStatistischer Testen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwFinanzmarkten_US
dc.subject.stwTheorieen_US
dc.subject.stwSequentialanalyseen_US
dc.titleOptimal sequential kernel detection for dependent processesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn373262949en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:sfb475:200327-
Appears in Collections:Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund

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