Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators

@article{Couillet2014LargeDA,
  title={Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators},
  author={Romain Couillet and Matthew R. McKay},
  journal={J. Multivariate Analysis},
  year={2014},
  volume={131},
  pages={99-120}
}
This article studies two regularized robust estimators of scatter matrices proposed (and proved to be well defined) in parallel in (Chen et al., 2011) and (Pascal et al., 2013), based on Tyler’s robust M-estimator (Tyler, 1987) and on Ledoit and Wolf’s shrinkage covariance matrix estimator (Ledoit and Wolf, 2004). These hybrid estimators have the advantage of conveying (i) robustness to outliers or impulsive samples and (ii) small sample size adequacy to the classical sample covariance matrix… CONTINUE READING
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