The uncertainty of a selected graphical model

Iris Pigeot, Fabian Sobotka, Svend Kreiner and Ronja Foraita

Journal of Applied Statistics, 2015, vol. 42, issue 11, 2335-2352

Abstract: Graphical models are useful to detect multivariate association structures in terms of conditional independencies and to represent these structures in a graph. When fitting graphical models to multivariate data, the uncertainty of a selected graphical model cannot be directly assessed. In this paper, we therefore propose various descriptive measures to assess the uncertainty of a graphical model based on the nonparametric bootstrap. We also introduce a so-called mean graphical model. Simulations and one real data example illustrate the application and interpretation of the newly proposed measures and demonstrate that the mean graphical model performs better than a single selected graphical model.

Date: 2015
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DOI: 10.1080/02664763.2015.1030368

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