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Technische Universität Dortmund >
Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen, Technische Universität Dortmund >
Technical Reports, SFB 475, TU Dortmund >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/49374
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Full metadata record
| DC Field | | Value | | Language |
| dc.contributor.author | | Steland, Ansgar | | en_US |
| dc.date.accessioned | | 2011-09-06T11:45:37Z | | - |
| dc.date.available | | 2011-09-06T11:45:37Z | | - |
| dc.date.issued | | 2003 | | en_US |
| dc.identifier.uri | | http://hdl.handle.net/10419/49374 | | - |
| dc.description.abstract | | In 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.iso | | eng | | en_US |
| dc.publisher | | Univ., SFB 475 Dortmund | | en_US |
| dc.relation.ispartofseries | | Technical Report // Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2003,27 | | en_US |
| dc.subject.ddc | | 330 | | en_US |
| dc.subject.keyword | | Enzyme kinetics | | en_US |
| dc.subject.keyword | | financial econometrics | | en_US |
| dc.subject.keyword | | nonparametric regression | | en_US |
| dc.subject.keyword | | statistical genetics | | en_US |
| dc.subject.keyword | | quality control | | en_US |
| dc.subject.stw | | Schätztheorie | | en_US |
| dc.subject.stw | | Statistischer Test | | en_US |
| dc.subject.stw | | Nichtparametrisches Verfahren | | en_US |
| dc.subject.stw | | Finanzmarkt | | en_US |
| dc.subject.stw | | Theorie | | en_US |
| dc.subject.stw | | Sequentialanalyse | | en_US |
| dc.title | | Optimal sequential kernel detection for dependent processes | | en_US |
| dc.type | | Working Paper | | en_US |
| dc.identifier.ppn | | 373262949 | | en_US |
| dc.rights | | http://www.econstor.eu/dspace/Nutzungsbedingungen | | en_US |
| dc.identifier.repec | | RePEc:zbw:sfb475:200327 | | - |
| Appears in Collections: | | Technical Reports, SFB 475, TU Dortmund
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