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dc.contributor.authorFried, Roland-
dc.contributor.authorVogel, Daniel-
dc.date.accessioned2009-12-16T09:51:55Z-
dc.date.available2009-12-16T09:51:55Z-
dc.date.issued2009-12-16T09:51:55Z-
dc.identifier.urihttp://hdl.handle.net/2003/26554-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15989-
dc.description.abstractThe objective of this exposition is to give an overview of the existing approaches to robust Gaussian graphical modelling. 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.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;36/2009-
dc.subjectCovariance selection modelen
dc.subjectGaussian graphical modelen
dc.subjectRobust methoden
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleOn robust Gaussian Graphical Modellingen
dc.typeTextde
dc.type.publicationtypereportde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 823

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