Sparse estimation of high-dimensional correlation matrices

Y. Cui, C. Leng, Defeng Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


© 2014 Elsevier B.V. All rights reserved. Several attempts to estimate covariance matrices with sparsity constraints have been made. A convex optimization formulation for estimating correlation matrices as opposed to covariance matrices is proposed. An efficient accelerated proximal gradient algorithm is developed, and it is shown that this method gives a faster rate of convergence. An adaptive version of this approach is also discussed. Simulation results and an analysis of a cardiovascular microarray confirm its performance and usefulness.
Original languageEnglish
Pages (from-to)390-403
Number of pages14
JournalComputational Statistics and Data Analysis
Publication statusPublished - 1 Jan 2016
Externally publishedYes


  • Accelerated proximal gradient
  • Correlation matrix
  • High-dimensionality
  • Positive definiteness
  • Sparsity

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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