NP-Optimal Kernels for Nonparametric Sequential Detection Rules

Verfasser / Beitragende:
[Ansgar Steland]
Ort, Verlag, Jahr:
2003
Enthalten in:
Economic Quality Control, 18/2(2003-10), 149-163
Format:
Artikel (online)
ID: 378847775
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024 7 0 |a 10.1515/EQC.2003.149  |2 doi 
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100 1 |a Steland  |D Ansgar  |u Ruhr-Universität Bochum, Fakultät für Mathematik, Mathematik 3 NA 3/71, Universitätsstr. 150, D-44780 Bochum, Germany. ansgar.steland@ruhr-uni-bochum.de 
245 1 0 |a NP-Optimal Kernels for Nonparametric Sequential Detection Rules  |h [Elektronische Daten]  |c [Ansgar Steland] 
520 3 |a 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. 
540 |a © Heldermann Verlag 
690 7 |a Control charts  |2 nationallicence 
690 7 |a financial data  |2 nationallicence 
690 7 |a nonparametric regression  |2 nationallicence 
690 7 |a quality control  |2 nationallicence 
690 7 |a statistical genetics  |2 nationallicence 
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950 |B NATIONALLICENCE  |P 100  |E 1-  |a Steland  |D Ansgar  |u Ruhr-Universität Bochum, Fakultät für Mathematik, Mathematik 3 NA 3/71, Universitätsstr. 150, D-44780 Bochum, Germany. ansgar.steland@ruhr-uni-bochum.de 
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