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Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables

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  • Fellinghauer, Bernd
  • Bühlmann, Peter
  • Ryffel, Martin
  • von Rhein, Michael
  • Reinhardt, Jan D.

Abstract

Random Forests in combination with Stability Selection allow to estimate stable conditional independence graphs with an error control mechanism for false positive selection. This approach is applicable to graphs containing both continuous and discrete variables at the same time. Its performance is evaluated in various simulation settings and compared with alternative approaches. Finally, the approach is applied to two heath-related data sets, first to study the interconnection of functional health components, personal, and environmental factors and second to identify risk factors which may be associated with adverse neurodevelopment after open-heart surgery.

Suggested Citation

  • Fellinghauer, Bernd & Bühlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.
  • Handle: RePEc:eee:csdana:v:64:y:2013:i:c:p:132-152
    DOI: 10.1016/j.csda.2013.02.022
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    References listed on IDEAS

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    6. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    7. Stanislav Kolenikov, 2009. "Confirmatory factor analysis using confa," Stata Journal, StataCorp LP, vol. 9(3), pages 329-373, September.
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    Cited by:

    1. Ashish Arora & Michelle Gittelman & Sarah Kaplan & John Lynch & Will Mitchell & Nicolaj Siggelkow & Mei Li & Ying Lin & Shuai Huang & Craig Crossland, 2016. "The use of sparse inverse covariance estimation for relationship detection and hypothesis generation in strategic management," Strategic Management Journal, Wiley Blackwell, vol. 37(1), pages 86-97, January.
    2. Bar-Hen, Avner & Poggi, Jean-Michel, 2016. "Influence measures and stability for graphical models," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 145-154.
    3. Chun, Hyonho & Lee, Myung Hee & Fleet, James C. & Oh, Ji Hwan, 2016. "Graphical models via joint quantile regression with component selection," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 162-171.
    4. Jianqing Fan & Han Liu & Yang Ning & Hui Zou, 2017. "High dimensional semiparametric latent graphical model for mixed data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 405-421, March.
    5. Peter Bühlmann & Florencia Leonardi, 2016. "Comments on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 239-246, June.
    6. Hirose, Kei & Fujisawa, Hironori & Sese, Jun, 2017. "Robust sparse Gaussian graphical modeling," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 172-190.

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