Partial Correlation Graphs and Dynamic Latent Variables for Physiological Time Series

  • Roland Fried
  • Vanessa Didelez
  • Vivian Lanius
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Latent variable techniques are helpful to reduce high-dimensional time series to a few relevant variables that are easier to model and analyze. An inherent problem is the identifiability of the model and the interpretation of the latent variables. We apply graphical models to find the essential relations in the data and to deduce suitable assumptions leading to meaningful latent variables.


Latent Variable Variable Selection Multivariate Time Series Dynamic Factor Model Group Factor Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. BRILLINGER, D.R. (1981): Time Series. Data Analysis and Theory. Holden Day, San Francisco.Google Scholar
  2. BRILLINGER, D.R. (1996): Remarks Concerning Graphical Models For Time Series And Point Processes. Revista de Econometria, 16, 1–23.MathSciNetGoogle Scholar
  3. DAHLHAUS, R. (2000): Graphical Interaction Models for Multivariate Time Series. Metrika, 51, 157–172.MATHMathSciNetCrossRefGoogle Scholar
  4. DAHLHAUS, R. and EICHLER, M. (2000): SPECTRUM. A C program to calculate and test partial spectral coherences. Available via Scholar
  5. DAVIES, P.L., FRIED, R., and GATHER, U. (2003): Robust Signal Extraction for On-line Monitoring Data. Journal of Statistical Planning and Inference, to appear.Google Scholar
  6. FORNI, M., HALLIN, M., LIPPI, M., and REICHLIN, L. (2000): The Generalized Dynamic Factor Model: Identification and Estimation. The Review of Economics and Statistics, 82, 540–554.CrossRefGoogle Scholar
  7. FRIED, R. (2003): Robust Filtering of Time Series with Trends. Technical Report 30/2003, SFB 475, University of Dortmund, Germany.Google Scholar
  8. FRIED, R. and DIDELEZ, V. (2003a): Decomposability and Selection of Graphical Models for Multivariate Time Series. Biometrika 90, 251–267.MathSciNetCrossRefGoogle Scholar
  9. FRIED, R. and DIDELEZ, V. (2003b): Latent Variable Analysis and Partial Correlation Graphs for Multivariate Time Series. Technical Report 6/2003, SFB 475, University of Dortmund, Germany.Google Scholar
  10. LAURITZEN, S.L. (1996): Graphical Models. Clarendon Press, Oxford.Google Scholar
  11. LANIUS, V. and GATHER, U. (2003): Dimension Reduction for Time Series from Intensive Care. Technical Report 2/2003, SFB 475, University of Dortmund, Germany.Google Scholar
  12. REINSEL, G.C. (1997): Elements of Multivariate Time Series Analysis. Second edition. Springer, New York.Google Scholar
  13. WHITTAKER, J. (1990): Graphical Models in Applied Multivariate Statistics. Wiley, Chichester.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Roland Fried
    • 1
  • Vanessa Didelez
    • 2
  • Vivian Lanius
    • 1
  1. 1.Fachbereich StatistikUniversität DortmundDortmundGermany
  2. 2.Department of Statistical ScienceUniversity College LondonLondonUK

Personalised recommendations