Latent variable analysis and partial correlation graphs for multivariate time series
AbstractWe 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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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 73 (2005)
Issue (Month): 3 (July)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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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