@inproceedings{0fe7d9eb4e5d4daba013ec09aff6f74a,
title = "Robust shrinkage estimation of high-dimensional covariance matrices",
abstract = "We address high dimensional covariance estimation for elliptical distributed samples. Specifically we consider shrinkage methods that are suitable for high dimensional problems with a small number of samples (large p small n). We start from a classical robust covariance estimator [Tyler(1987)], which is distribution-free within the family of elliptical distribution but inapplicable when n < p. Using a shrinkage coefficient, we regularize Tyler's fixed point iteration. We derive the minimum mean-squared-error shrinkage coefficient in closed form. The closed form expression is a function of the unknown true covariance and cannot be implemented in practice. Instead, we propose a plug-in estimate to approximate it. Simulations demonstrate that the proposed method achieves low estimation error and is robust to heavy-tailed samples.",
author = "Yilun Chen and Ami Wiesel and Hero, {Alfred O.}",
year = "2010",
doi = "10.1109/SAM.2010.5606730",
language = "American English",
isbn = "9781424489770",
series = "2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010",
pages = "189--192",
booktitle = "2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010",
note = "2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010 ; Conference date: 04-10-2010 Through 07-10-2010",
}