Latent variable analysis and partial correlation graphs for multivariate time series
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.
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Volume (Year): 73 (2005)
Issue (Month): 3 (July)
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- Roland Fried, 2003. "Decomposability and selection of graphical models for multivariate time series," Biometrika, Biometrika Trust, vol. 90(2), pages 251-267, June.
- Gather, Ursula & Fried, Roland & Lanius, Vivian & Imhoff, Michael, 2001. "Online monitoring of high dimensional physiological time series: A case study," Technical Reports 2001,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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