Multiple Testing and Error Control in Gaussian Graphical Model Selection

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dc.creatorDrton, Mathias
dc.creatorPerlman, Michael D.
dc.date2005-08-15
dc.date2008-02-05
dc.date.accessioned2026-07-07T09:19:21Z
dc.date.available2026-07-07T09:19:21Z
dc.descriptionGraphical models provide a framework for exploration of multivariate dependence patterns. The connection between graph and statistical model is made by identifying the vertices of the graph with the observed variables and translating the pattern of edges in the graph into a pattern of conditional independences that is imposed on the variables' joint distribution. Focusing on Gaussian models, we review classical graphical models. For these models the defining conditional independences are equivalent to vanishing of certain (partial) correlation coefficients associated with individual edges that are absent from the graph. Hence, Gaussian graphical model selection can be performed by multiple testing of hypotheses about vanishing (partial) correlation coefficients. We show and exemplify how this approach allows one to perform model selection while controlling error rates for incorrect edge inclusion.
dc.descriptionPublished in at http://dx.doi.org/10.1214/088342307000000113 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0508267
dc.identifierhttp://arxiv.org/abs/math/0508267
dc.identifierStatistical Science 2007, Vol. 22, No. 3, 430-449
dc.identifierdoi:10.1214/088342307000000113
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/154357
dc.subjectStatistics Theory
dc.titleMultiple Testing and Error Control in Gaussian Graphical Model Selection
dc.typetext

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