On Robust Gaussian Graphical Modeling

Daniel Vogel () and Roland Fried ()
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Daniel Vogel: Technische Universität Dortmund, Fakultät Statistik
Roland Fried: Technische Universität Dortmund, Fakultät Statistik

A chapter in Recent Developments in Applied Probability and Statistics, 2010, pp 155-182 from Springer

Abstract: 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.

Keywords: Partial Correlation; Graphical Model; Conditional Independence; Robust Estimator; Sample Covariance Matrix (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2598-5_7

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DOI: 10.1007/978-3-7908-2598-5_7

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