Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/49347 
Year of Publication: 
2003
Series/Report no.: 
Technical Report No. 2003,30
Publisher: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Abstract: 
We develop and test a robust procedure for extracting an underlying signal in form of a time-varying trend from very noisy time series. The application we have in mind is online monitoring data measured in intensive care, where we find periods of relative constancy, slow monotonic trends, level shifts and many measurement artifacts. A procedure is needed which allows a fast and reliable denoising of the data and which distinguishes artifacts from clinically relevant changes in the patient's condition. We use robust regression functionals for local approximation of the trend in a moving time window. For further improving the robustness of the procedure we investigate online outlier replacement by e.g. trimming or winsorization based on robust scale estimators. The performance of several versions of the procedure is compared in important data situations and applications to real and simulated data are given.
Subjects: 
Online monitoring
Signal extraction
Level shift
Trend
Outlier
Bias curve
Document Type: 
Working Paper

Files in This Item:
File
Size
350.86 kB
1.23 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.