Authors: Steland, Ansgar
Title: Sequential Control of Time Series by Functionals of Kernel-Weighted Empirical Processes under Local Alternatives
Language (ISO): en
Abstract: Motivated in part by applications in model selection in statistical genetics and sequential monitoring of financial data, we study an empirical process framework for a class of stopping rules which rely on kernel-weighted averages of past data. We are interested in the asymptotic distribution for time series data and an analysis of the joint influence of the smoothing policy and the alternative defining the deviation from the null model (in-control state). We employ a certain type of local alternative which provides meaningful insights. Our results hold true for short memory processes which satisfy a weak mixing condition. By relying on an empirical process framework we obtain both asymptotic laws for the classical fixed sample design and the sequential monitoring design. As a by-product we establish the asymptotic distribution of the Nadaraya-Watson kernel smoother when the regressors do not get dense as the sample size increases.
Subject Headings: control chart
sequential test
statistical genetics
Issue Date: 2003
Provenance: Universitätsbibliothek Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
19_03.pdfDNB219.83 kBAdobe PDFView/Open
tr19-03.ps440.48 kBPostscriptView/Open

This item is protected by original copyright

All resources in the repository are protected by copyright.