Estimating the covariance of random matrices

Research output: Contribution to journalArticlepeer-review


We extend to the matrix setting a recent result of Srivastava-Vershynin [24] about estimating the covariance matrix of a random vector. The result can be interpreted as a quantified version of the law of large numbers for positive semi-definite matrices which verify some regularity assumption. Beside giving examples, we discuss the notion of log-concave matrices and give estimates on the smallest and largest eigenvalues of a sum of such matrices.

Original languageEnglish (US)
Article number107
JournalElectronic Journal of Probability
StatePublished - Dec 19 2013


  • Covariance matrix
  • Log-concave matrix

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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