Partial Correlation Graphs and Dynamic Latent Variables for Physiological Time Series
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.
Unable to display preview. Download preview PDF.
- BRILLINGER, D.R. (1981): Time Series. Data Analysis and Theory. Holden Day, San Francisco.Google Scholar
- DAHLHAUS, R. and EICHLER, M. (2000): SPECTRUM. A C program to calculate and test partial spectral coherences. Available via http://www.statlab.uni-heidelberg.de/projects/.Google Scholar
- 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
- FRIED, R. (2003): Robust Filtering of Time Series with Trends. Technical Report 30/2003, SFB 475, University of Dortmund, Germany.Google Scholar
- 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
- LAURITZEN, S.L. (1996): Graphical Models. Clarendon Press, Oxford.Google Scholar
- 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
- REINSEL, G.C. (1997): Elements of Multivariate Time Series Analysis. Second edition. Springer, New York.Google Scholar
- WHITTAKER, J. (1990): Graphical Models in Applied Multivariate Statistics. Wiley, Chichester.Google Scholar