NDLI: Robust shrinkage estimation of high-dimensional covariance matrices

Content Provider	IEEE Xplore Digital Library
Author	Yilun Chen Wiesel, A. Hero, A.O.
Copyright Year	2010
Description	Author affiliation: Department of EECS, University of Michigan, Ann Arbor, USA (Yilun Chen; Hero, A.O.) \|\| Department of CS, Hebrew University, Jerusalem, Israel (Wiesel, A.)
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.
Starting Page	189
Ending Page	192
File Size	371771
Page Count	4
File Format	PDF
ISBN	9781424489787
ISSN	2151870X
e-ISBN	9781424489770
e-ISBN	9781424493951
DOI	10.1109/SAM.2010.5606730
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-04
Publisher Place	Israel
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Covariance matrix Robustness Signal processing Clutter Signal processing algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Regularized robust estimation of mean and covariance matrix under heavy tails and outliers

Shrinkage covariance matrix estimator applied to STAP detection

Robust Shrinkage Estimation of High-Dimensional Covariance Matrices

Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributions

Robust maximum-likelihood estimation of structured covariance matrices

Robust estimation of the clutter subspace for a Low Rank heterogeneous noise under high Clutter to Noise Ratio assumption

Double shrinkage correction in sample LMMSE estimation

Efficient reiterative censoring of robust STAP using the FRACTA algorithm

On persymmetric covariance matrices in adaptive detection

Robust shrinkage estimation of high-dimensional covariance matrices

Similar Documents

Regularized robust estimation of mean and covariance matrix under heavy tails and outliers

Shrinkage covariance matrix estimator applied to STAP detection

Robust Shrinkage Estimation of High-Dimensional Covariance Matrices

Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributions

Robust maximum-likelihood estimation of structured covariance matrices

Robust estimation of the clutter subspace for a Low Rank heterogeneous noise under high Clutter to Noise Ratio assumption

Double shrinkage correction in sample LMMSE estimation

Efficient reiterative censoring of robust STAP using the FRACTA algorithm

On persymmetric covariance matrices in adaptive detection

Robust shrinkage estimation of high-dimensional covariance matrices