Elliptical graphical modelling

Daniel Vogel, Roland Fried

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


We propose elliptical graphical models based on conditional uncorrelatedness as a robust generalization of Gaussian graphical models. Letting the population distribution be elliptical instead of normal allows the fitting of data with arbitrarily heavy tails. We study the class of proportionally affine equivariant scatter estimators and show how they can be used to perform elliptical graphical modelling. This leads to a new class of partial correlation estimators and analogues of the classical deviance test. General expressions for the asymptotic variance of partial correlation estimators, unconstrained and under decomposable models, are given, and the asymptotic chi square approximation for the pseudo-deviance test statistic is proved. The feasibility of our approach is demonstrated by a simulation study, using, among others, Tyler’s scatter estimator, which is distribution-free within the elliptical model.
Original languageEnglish
Pages (from-to)935-951
Number of pages17
Issue number4
Early online date13 Oct 2011
Publication statusPublished - Dec 2011


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