Advertisement

Comparison of Statistical Procedures for Gaussian Graphical Model Selection

  • Ivan S. GrechikhinEmail author
  • Valery A. Kalyagin
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 247)

Abstract

Graphical models are used in a variety of problems to uncover hidden structures. There is an important number of different identification procedures to recover graphical model from observations. In this paper, undirected Gaussian graphical models are considered. Some Gaussian graphical model identification statistical procedures are compared using different measures, such as Type I and Type II errors, ROC AUC.

Keywords

Gaussian graphical model Identification procedure Statistical inference Risk function ROC AUC 

Notes

Acknowledgements

The work is partially supported by RFBR grant 18-07-00524.

References

  1. 1.
    Mathias, D., Perlman, M.D.: Multiple testing and error control in gaussian graphical model selection. Stat. Sci. 22(3), 430–449 (2007)Google Scholar
  2. 2.
    Jordan, M.I.: Graphical models. Stat. Sci. 19(3), 140–155 (2004)Google Scholar
  3. 3.
    Drton, M., Perlman, M.D.: A SINful approach to Gaussian graphical model selection. J. Stat. Plan. Inference 138, 1179–1200 (2008)Google Scholar
  4. 4.
    Gottard, A., Pacillo, S.: Robust concentration graph model selection. Comput. Stat. Data Anal. 54, 3070–3079 (2010)Google Scholar
  5. 5.
    Schafer, J., Strimmer, K.: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6), 754–764 (2005)Google Scholar
  6. 6.
    Khondker, Z.S., et al.: The Bayesian covariance lasso. Stat Interface 6(2), 243–259 (2013)Google Scholar
  7. 7.
    Yuan, M., Lin, Y.: Model selection and estimation in the Gaussian graphical model. Biometrika 94(1), 19–35 (2007)Google Scholar
  8. 8.
    Kalyagin, V.A., Koldanov, A.P., Koldanov, P.A., Pardalos, P.M.: Optimal statistical decision for Gaussian graphical model selection. Cornell University Library (stat.ML). arXiv:1701.02071

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Algorithms and Technologies for Network AnalysisNational Research University Higher School of EconomicsNizhny NovgorodRussia

Personalised recommendations