MINIMAX ESTIMATION OF LARGE COVARIANCE MATRICES UNDER ℓ1-NORM

No abstract is available for this paper.

• Publication year

  2012

• Venue

  Statistica Sinica

• Publication date

  Unknown publication date

• Fields of study

  Mathematics

• Identifiers
  DOI 10.5705/SS.2010.253
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Optimal rates of convergence for estimating Toeplitz covariance matrices

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  2012influential reference
• Optimal rates of convergence for covariance matrix estimation

  2010cited by this paper
• Covariance regularization by thresholding

  2009influential reference
• Sparse Principal Components Analysis

  2009cited by this paper
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions

  2009cited by this paper
• Regularized estimation of large covariance matrices

  2008influential reference
• Operator norm consistent estimation of large-dimensional sparse covariance matrices

  2008cited by this paper
• Introduction to Nonparametric Estimation

  2008cited by this paper
• Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation.

  2007cited by this paper
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  2005cited by this paper
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  1991cited by this paper
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  1983influential reference
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  1973influential reference
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