Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/77287 
Year of Publication: 
2001
Series/Report no.: 
Technical Report No. 2001,14
Publisher: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Abstract: 
Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which was developed to reduce the dimension of the regressor in regression problems, as an exploratory tool for analyzing multivariate time series. Analyzing each variable individually, we search for those directions, i.e., linear combinations of past and present observations of the other variables which explain most of the variability of the variable considered. This can also provide information on possible nonlinearities. We apply a dynamic version of SIR to multivariate physiological time series observed in intensive care.
Subjects: 
Time series analysis
Nonlinearities
Dimension Reduction
Document Type: 
Working Paper

Files in This Item:
File
Size
224.6 kB





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