Authors: Steland, Ansgar
Title: Optimal Sequential Kernel Detection for Dependent Processes
Language (ISO): en
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.
Subject Headings: enzyme kinetics
financial econometrics
nonparametric regression
statistical genetics
quality control
URI: http://hdl.handle.net/2003/4989
http://dx.doi.org/10.17877/DE290R-5528
Issue Date: 2003
Publisher: Universitätsbibliothek Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

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