Robust estimators for nondecomposable elliptical graphical models

D. Vogel, D. E. Tyler

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Robust estimators of the restricted covariance matrices associated with elliptical graphical models are studied. General asymptotic results, which apply to both decomposable and nondecomposable graphical models, are presented for robust plug-in type estimators. These extend results previously established only for the decomposable case. Furthermore, a class of graphical M-estimators for the restricted covariance matrices is introduced and compared with the corresponding plug-in M-estimators. The two approaches are shown to be asymptotically equivalent under random sampling from an elliptical distribution. A simulation study demonstrates the superiority of the graphical M-estimators for small samples.

Original languageEnglish (US)
Pages (from-to)865-882
Number of pages18
Issue number4
StatePublished - Dec 1 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


  • Affine equivariance
  • Deviance test
  • Gaussian graphical model
  • M-estimator
  • Partial correlation


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