Skip to Main content Skip to Navigation
Journal articles

Robust Estimates of Covariance Matrices in Large Dimensional Regime

Abstract : This paper studies the limiting behavior of a class of robust population covariance matrix estimators, originally due to Maronna in 1976, in the regime where both the number of available samples and the population size grow large. Using tools from random matrix theory, we prove that, for sample vectors made of independent entries having some moment conditions, the difference between the sample covariance matrix and (a scaled version of) such robust estimator tends to zero in spectral norm, almost surely. This result can be applied to various statistical methods arising from random matrix theory that can be made robust without altering their first order behavior.
Document type :
Journal articles
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download
Contributor : Virginie Bouvier Connect in order to contact the contributor
Submitted on : Wednesday, March 4, 2020 - 5:46:19 PM
Last modification on : Tuesday, October 19, 2021 - 12:52:59 PM
Long-term archiving on: : Friday, June 5, 2020 - 3:59:12 PM


Files produced by the author(s)



Romain Couillet, Frédéric Pascal, Jack W. Silverstein. Robust Estimates of Covariance Matrices in Large Dimensional Regime. IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2014, 60 (11), pp.7269 - 7278. ⟨10.1109/TIT.2014.2354045⟩. ⟨hal-01104000⟩



Les métriques sont temporairement indisponibles