A well-conditioned estimator for large-dimensional covariance matrices

@inproceedings{Ledoit2004AWE,
  title={A well-conditioned estimator for large-dimensional covariance matrices},
  author={Olivier Ledoit and Michael Wolf},
  year={2004}
}
Many applied problems require a covariance matrix estimator that is not only invertible, but also well-conditioned (that is, inverting it does not amplify estimation error). For large-dimensional covariance matrices, the usual estimator--the sample covariance matrix--is typically not well-conditioned and may not even be invertible. This paper introduces an estimator that is both well-conditioned and more accurate than the sample covariance matrix asymptotically. This estimator is distribution… CONTINUE READING

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