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The uncertainty of a selected graphical model

Author

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  • Iris Pigeot
  • Fabian Sobotka
  • Svend Kreiner
  • Ronja Foraita

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.

Suggested Citation

  • Iris Pigeot & Fabian Sobotka & Svend Kreiner & Ronja Foraita, 2015. "The uncertainty of a selected graphical model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(11), pages 2335-2352, November.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2335-2352
    DOI: 10.1080/02664763.2015.1030368
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    Cited by:

    1. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.

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