@inproceedings{2474ad4cd869446e862c4786464e588e,
title = "Large covariance matrix estimation: Bridging shrinkage and tapering approaches",
abstract = "In this paper, we propose a shrinkage-to-tapering oracle (STO) estimator for estimation of large covariance matrix when the number of samples is substantially fewer than the number of variables, by combining the strength from both Steinian-type shrinkage and tapering estimators. Our contributions include: (i) Deriving the Frobenius risk and a lower bound for the spectral risk of an MMSE shrinkage estimator; (ii) Deriving a closed-form expression for the optimal coefficient of the proposed STO estimator. Simulations on auto-regression (e.g. a sparse case) and fraction Brownian motion (e.g. a non-sparse case) covariance structures are used to demonstrate the superiority of the proposed estimator.",
keywords = "Covariance matrix, high-dimensionality, shrinkage estimator, tapering estimator",
author = "Xiaohui Chen and Wang, {Z. Jane} and McKeown, {Martin J.}",
year = "2012",
month = oct,
day = "23",
doi = "10.1109/ICASSP.2012.6288303",
language = "English (US)",
isbn = "9781467300469",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "2013--2016",
booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",
note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",
}