Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/49370 
Year of Publication: 
2003
Series/Report no.: 
Technical Report No. 2003,06
Publisher: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Abstract: 
We investigate the possibility of exploiting partial correlation graphs for identifying interpretable latent variables underlying a multivariate time series. It is shown how the collapsibility and separation properties of partial correlation graphs can be used to understand the relation between a factor model and the structure among the observable variables.
Subjects: 
Time series analysis
Dimension reduction
Factor analysis
Partial correlations
Document Type: 
Working Paper

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