NP-Optimal Kernels for Nonparametric Sequential Detection Rules

dc.contributor.author	Steland, Ansgar	de
dc.date.accessioned	2004-12-06T18:38:43Z
dc.date.available	2004-12-06T18:38:43Z
dc.date.issued	2004
dc.description.abstract	An attractive nonparametric method to detect change-points sequentially is to apply control charts based on kernel smoothers. Recently, the strong convergence of the associated normed delay associated with such a sequential stopping rule has been studied under sequences of out-of-control models. Kernel smoothers employ a kernel function to downweight past data. Since kernel functions with values in the unit interval are sufficient for that task, we study the problem to optimize the asymptotic normed delay over a class of kernels ensuring that restriction and certain additional moment constraints. We apply the key theorem to discuss several important examples where explicit solutions exist to illustrate that the results are applicable.	en
dc.format.extent	211798 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/4865
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15068
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	control charts	en
dc.subject	financial data	en
dc.subject	nonparametric regression	en
dc.subject	quality control	en
dc.subject	statistical genetics	en
dc.subject.ddc	310	de
dc.title	NP-Optimal Kernels for Nonparametric Sequential Detection Rules	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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