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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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    1. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Archer, Kellie J., 2010. "rpartOrdinal: An R Package for Deriving a Classification Tree for Predicting an Ordinal Response," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i07).
    4. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    5. Jan Reinhardt & Ulrich Mansmann & Bernd Fellinghauer & Ralf Strobl & Eva Grill & Erik Elm & Gerold Stucki, 2011. "Functioning and disability in people living with spinal cord injury in high- and low-resourced countries: a comparative analysis of 14 countries," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 56(3), pages 341-352, June.
    6. Hapfelmeier, A. & Hothorn, T. & Ulm, K., 2012. "Recursive partitioning on incomplete data using surrogate decisions and multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1552-1565.
    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:

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    2. Sheng, Tianhong & Li, Bing & Solea, Eftychia, 2023. "On skewed Gaussian graphical models," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    3. 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.
    4. Kim, Kyongwon, 2022. "On principal graphical models with application to gene network," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
    5. 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.
    6. 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.
    7. Linh H. Nghiem & Francis K. C. Hui & Samuel Müller & Alan H. Welsh, 2022. "Estimation of graphical models for skew continuous data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1811-1841, December.
    8. 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.
    9. 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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