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Latent variable analysis and partial correlation graphs for multivariate time series

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  • Fried, Roland
  • Didelez, Vanessa

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.

Suggested Citation

  • Fried, Roland & Didelez, Vanessa, 2005. "Latent variable analysis and partial correlation graphs for multivariate time series," Statistics & Probability Letters, Elsevier, vol. 73(3), pages 287-296, July.
  • Handle: RePEc:eee:stapro:v:73:y:2005:i:3:p:287-296
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    References listed on IDEAS

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    1. repec:sbe:breart:v:16:y:1996:i:1:a:2878 is not listed on IDEAS
    2. Roland Fried, 2003. "Decomposability and selection of graphical models for multivariate time series," Biometrika, Biometrika Trust, vol. 90(2), pages 251-267, June.
    3. 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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    Cited by:

    1. Baek, Changryong & Davis, Richard A. & Pipiras, Vladas, 2017. "Sparse seasonal and periodic vector autoregressive modeling," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 103-126.

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