Improved Shrinkage Estimator of Large-Dimensional Covariance Matrix under the Complex Gaussian Distribution
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-07
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Estimating the covariance matrix of a random vector is essential and challenging in large dimension and small sample size scenarios.
The purpose of this paper is to produce an outperformed large-dimensional covariance matrix estimator in the complex domain via the linear shrinkage regularization.
Firstly, we develop a necessary moment property of the complex Wishart distribution.
Secondly, by minimizing the mean squared error between the real covariance matrix and its shrinkage estimator, we obtain the optimal shrinkage intensity in a closed form for the spherical target matrix under the complex Gaussian distribution.
Thirdly, we propose a newly available shrinkage estimator by unbiasedly estimating the unknown scalars involved in the optimal shrinkage intensity.
Both the numerical simulations and an example application to array signal processing reveal that the proposed covariance matrix estimator performs well in large dimension and small sample size scenarios.
American Psychological Association (APA)
Zhang, Bin. 2020. Improved Shrinkage Estimator of Large-Dimensional Covariance Matrix under the Complex Gaussian Distribution. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1196842
Modern Language Association (MLA)
Zhang, Bin. Improved Shrinkage Estimator of Large-Dimensional Covariance Matrix under the Complex Gaussian Distribution. Mathematical Problems in Engineering No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1196842
American Medical Association (AMA)
Zhang, Bin. Improved Shrinkage Estimator of Large-Dimensional Covariance Matrix under the Complex Gaussian Distribution. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1196842
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196842