High-dimensional covariance matrix estimation with missing observations

@article{Lounici2012HighdimensionalCM,
  title={High-dimensional covariance matrix estimation with missing observations},
  author={Karim Lounici},
  journal={arXiv: Statistics Theory},
  year={2012},
  pages={1029-1058}
}
  • Karim Lounici
  • Published 2012
  • Mathematics
  • arXiv: Statistics Theory
  • In this paper, we study the problem of high-dimensional approximately low-rank covariance matrix estimation with missing observations. We propose a simple procedure computationally tractable in high-dimension and that does not require imputation of the missing data. We establish non-asymptotic sparsity oracle inequalities for the estimation of the covariance matrix with the Frobenius and spectral norms, valid for any setting of the sample size and the dimension of the observations. We further… CONTINUE READING

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