On Robust Gaussian Graphical Modeling


The objective of this exposition is to give an overview of the existing approaches to robust Gaussian graphical modeling. We start by thoroughly introducing Gaussian graphical models (also known as covariance selection models or concentration graph models) and then review the established, likelihood-based statistical theory (estimation, testing and model selection). Afterwards we describe robust methods and compare them to the classical approaches.


Partial Correlation Graphical Model Conditional Independence Robust Estimator Sample Covariance Matrix 
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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Fakultät StatistikTechnische Universität DortmundDortmundGermany

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