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Sparse estimation of high-dimensional correlation matrices

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  • Cui, Ying
  • Leng, Chenlei
  • Sun, Defeng

Abstract

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.

Suggested Citation

  • Cui, Ying & Leng, Chenlei & Sun, Defeng, 2016. "Sparse estimation of high-dimensional correlation matrices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 390-403.
    • Handle: RePEc:eee:csdana:v:93:y:2016:i:c:p:390-403
    DOI: 10.1016/j.csda.2014.10.001
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    References listed on IDEAS

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    Cited by:

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    8. Tianyu Cui & Francesco Caravelli & Cozmin Ududec, 2017. "Correlations and Clustering in Wholesale Electricity Markets," Papers 1710.11184, arXiv.org, revised Nov 2017.
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