Sie sind hier: Startseite Seminar Prof. Dr. Roland Fried

Prof. Dr. Roland Fried

— abgelegt unter:

  • FDM-Seminar
Wann 29.01.2010
von 11:15 bis 13:00
Wo Eckerstr. 1, Raum 404
Kontakttelefon 0761/203-7701
Termin übernehmen vCal

Robust partial correlation graphs


Roland Fried

Daniel Vogel

Technische Universität Dortmund,
44221 Dortmund, Germany


Graphical models allow a simple graphical visualization of the (conditional) dependence structure among multiple variables: each variable is represented by a vertex, and conditional dependence between a pair of variables given all the other variables is illustrated by connecting the corresponding pair of vertices by an edge. Within the Gaussian framework, conditional independence is equivalent to zero partial correlation, i.e. we just need to estimate the partial correlation between each pair of variables and test whether it is zero or not in order to decide on the inclusion of edges in the graph. The arising diagram can thus be called a partial correlation graph.

In this talk we treat two extensions of Gaussian graphical models. Brillinger (1996) and Dahlhaus (2000) suggest to explore the linear dependence structure among multivariate time series by analyzing the partial spectral coherences between the component processes. These are a natural generalization of the partial correlations to the frequency domain. Fried and Didelez (2003) show how to perform stepwise model selection in this context by estimating the partial spectral coherences from suitably chosen subsets of the component processes.

Another generalization of Gaussian graphical models are elliptical graphical models, that is, we allow the population distribution to be elliptical instead of normal. We examine the class of affine equivariant scatter estimators and show how they can be used to derive generalizations of classical Gaussian graphical modelling tools derived from the empirical covariance matrix and the adjusted deviance tests. We demonstrate the feasibility of our approach by a simulation study, using, among others, Tyler's scatter estimator (Tyler, 1987), which is distribution-free within the elliptical model. This technique is in particular suited to robustify the established, likelihood-based Gaussian graphical modelling methods, which are known to be very sensitive to model misspecifications and outlying observations. Some of the results are summarized in Vogel and Fried (2009).

The robust fitting of partial correlation graphs to multivariate time series data, e.g. by extending the ideas of elliptical graphical modelling to the time series context, is the scope of future research.




[1]  D. R. Brillinger. Remarks concerning graphical models for time series and point
      processes. Revista de Econometria, 16:1-23, 1996.

[2]  R. Dahlhaus. Graphical interaction models for multivariate time series.
      Metrika, 51:157-172, 2000.

[3]  R. Fried and V. Didelez. Decomposability and selection of graphical models for
      multivariate time series. Biometrika, 90: 251-267, 2003.

[4]  D. E. Tyler. A distribution-free Mestimator of multivariate scatter.
      Annals of Statistics, 15:234-251, 1987.

[5]  D. Vogel and R. Fried. On robust Gaussian graphical modelling.
      Discussion Paper 36/2009, SFB 823, Technische UniversitÄat Dortmund, 2009.

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