Detection performance of Roy's largest root test when the noise covariance matrix is arbitrary

IEEE Workshop on Statistical Signal Processing Proceedings

Volumn , Issue , 2011, Pages 681-684

  Nadler, B ^a   Johnstone, I M ^b  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

^b STANFORD UNIVERSITY   (United States)

Author keywords

inverse Wishart distribution; matrix perturbation; Roy's largest root test; signal detection

Indexed keywords

APPROXIMATE EXPRESSIONS; DETECTION PERFORMANCE; DETECTION PROBABILITIES; EIGENVALUES; FUNDAMENTAL PROBLEM; LARGEST EIGENVALUES; MATRIX PERTURBATION; MULTI-SENSOR SYSTEMS; NOISE COVARIANCE MATRIX; ROY'S LARGEST ROOT TEST; WISHART DISTRIBUTION; WISHART MATRICES;

ARRAY PROCESSING; BEHAVIORAL RESEARCH; COVARIANCE MATRIX; EIGENVALUES AND EIGENFUNCTIONS;

SIGNAL DETECTION;

EID: 80052190956     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSP.2011.5967793     Document Type: Conference Paper

References (7)

1. S. Kritchman and B. Nadler, "Nonparametric detection of the number of signals, hypothesis testing and random matrix theory," IEEE Tran. Sig. Proc., vol. 57, pp. 3930-41, 2009.
2. L.C. Zhao, P.R. Krishnaiah, and Z.D. Bai, "On detection of the number of signals when the noise covariance matrix is arbitrary," J.Multivariate Anal., vol. 20, pp. 26-49, 1986.
3. R. Rao Nadakuditi and J.W. Silverstein, "Fundamental limit of sample generalized eigenvalue based detection of signals in noise using relatively few signal-bearing and noise-only samples," IEEE Selected Topics in Sig. Proc., vol. 4, no. 3, pp. 468-480, 2010.
4. I.M. Johnstone, "Multivariate analysis and Jacobi ensembles," Ann. Stat., vol. 36, no. 6, pp. 2638-2716, 2008.
5. J. Baik and J.W. Silverstein, "Eigenvalues of large sample covariance matrices of spiked population models," J. Multivariate Anal., vol. 97, pp. 1382-1408, 2006.
6. D. Paul, "Asymptotics of sample eigenstructure for a large dimensional spiked covariance model," Stat. Sinica, vol. 17, pp. 1617-1642, 2007.
7. B. Nadler, "Finite sample approximation results for principal component analysis: A matrix perturbation approach," Ann. Stat., vol. 36, no. 6, pp. 2791-2817, 2008.