Partial Correlation Graphs and Dynamic Latent Variables for Physiological Time Series

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

Abstract

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.

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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

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