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Recent Developments in Applied Probability and Statistics pp 155–182Cite as

On Robust Gaussian Graphical Modeling

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Abstract

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.

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Authors and Affiliations

  1. Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany

    Daniel Vogel & Roland Fried

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  1. Daniel Vogel
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  2. Roland Fried
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Correspondence to Daniel Vogel .

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Editors and Affiliations

  1. , School of Computer Science, McGill University, University St., Montreal, H9X 3V9, Canada

    Luc Devroye

  2. , Mathematics & Institute of Applied Mathe, Middle East Technical University, Inonu Bulvari, Ankara, 06531, Turkey

    Bülent Karasözen

  3. FB Mathematik, TU Darmstadt, Schloßgartenstr. 7, Darmstadt, 64289, Germany

    Michael Kohler

  4. , Mathematics, University of Kaiserslautern, E.-Schrödinger-Str., Kaiserslautern, 67663, Germany

    Ralf Korn

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Vogel, D., Fried, R. (2010). On Robust Gaussian Graphical Modeling. In: Devroye, L., Karasözen, B., Kohler, M., Korn, R. (eds) Recent Developments in Applied Probability and Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2598-5_7

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