Jack W Silverstein

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Romain COUILLETUGA IDEX DataScience Chair, GIPSA-lab, University Grenoble-AlpesDirección de correo verificada de gipsa-lab.grenoble-inp.fr
Merouane DebbahKU 6G Center, Khalifa University, CentralesupelecDirección de correo verificada de centralesupelec.fr
Frédéric PascalProfessor, L2S/CentraleSupélecDirección de correo verificada de centralesupelec.fr
Jinho BaikDepartment of Mathematics, University of MichiganDirección de correo verificada de umich.edu
Raj Rao NadakuditiAssociate Professor, University of MichiganDirección de correo verificada de umich.edu
Patrick L. CombettesDepartment of Mathematics, North Carolina State UniversityDirección de correo verificada de math.ncsu.edu
Wlodek BrycProfessor of Mathematical Sciences, University of CincinnatiDirección de correo verificada de uc.edu
Brian RiderProfessor, Temple UniversityDirección de correo verificada de temple.edu
Jamal NajimCNRS et Université Gustave Eiffel (Paris Est)Dirección de correo verificada de univ-eiffel.fr
Jianfeng (Jeff) YaoThe Chinese University of Hong Kong (Shenzhen)Dirección de correo verificada de cuhk.edu.cn
Qinwen WANGFudan UniversityDirección de correo verificada de fudan.edu.cn
Antonia Maria TulinoUniverisità Federico II, Napoli, Italy - New York University, NY USA, - Bell Labs, NJ, USADirección de correo verificada de unina.it
Jiang HuNortheast Normal UniversityDirección de correo verificada de nenu.edu.cn
Yanting MaMitsubishi Electric Research LaboratoriesDirección de correo verificada de merl.com
Dror BaronNorth Carolina State UniversityDirección de correo verificada de ncsu.edu
Min KangDepartment of Mathematics, North Carolina State UniversityDirección de correo verificada de ncsu.edu
Katarzyna (Kasia) BrycSenior Scientist, Population Genetics

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Jack W Silverstein

North Carolina State University

Dirección de correo verificada de math.ncsu.edu - Página principal

Random matrices


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Spectral analysis of large dimensional random matrices Z Bai, JW Silverstein Springer, 2010	2165	2010
Distinctive features, categorical perception, and probability learning: Some applications of a neural model. JA Anderson, JW Silverstein, SA Ritz, RS Jones Psychological review 84 (5), 413, 1977	1430	1977
On the empirical distribution of eigenvalues of a class of large dimensional random matrices JW Silverstein, ZD Bai Journal of Multivariate analysis 54 (2), 175-192, 1995	869	1995
Eigenvalues of large sample covariance matrices of spiked population models J Baik, JW Silverstein Journal of multivariate analysis 97 (6), 1382-1408, 2006	820	2006
CLT for linear spectral statistics of large-dimensional sample covariance matrices ZD Bai, JW Silverstein Advances In Statistics, 281-333, 2008	660	2008
No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices ZD Bai, JW Silverstein The Annals of Probability 26 (1), 316-345, 1998	645	1998
Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices JW Silverstein Journal of Multivariate Analysis 55 (2), 331-339, 1995	593	1995
Analysis of the limiting spectral distribution of large dimensional random matrices JW Silverstein, SI Choi Journal of Multivariate Analysis 54 (2), 295-309, 1995	378	1995
The smallest eigenvalue of a large dimensional Wishart matrix JW Silverstein The Annals of Probability, 1364-1368, 1985	343	1985
A deterministic equivalent for the analysis of correlated MIMO multiple access channels R Couillet, M Debbah, JW Silverstein IEEE Transactions on Information Theory 57 (6), 3493-3514, 2011	221	2011
A note on the largest eigenvalue of a large dimensional sample covariance matrix ZD Bai, JW Silverstein, YQ Yin Journal of Multivariate Analysis 26 (2), 166-168, 1988	217	1988
On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices RB Dozier, JW Silverstein Journal of Multivariate Analysis 98 (4), 678-694, 2007	182	2007
Fundamental limit of sample generalized eigenvalue based detection of signals in noise using relatively few signal-bearing and noise-only samples RR Nadakuditi, JW Silverstein IEEE Journal of selected topics in Signal Processing 4 (3), 468-480, 2010	180	2010
Exact separation of eigenvalues of large dimensional sample covariance matrices ZD Bai, JW Silverstein Annals of probability, 1536-1555, 1999	174	1999
Spectral analysis of networks with random topologies U Grenander, JW Silverstein SIAM Journal on Applied Mathematics 32 (2), 499-519, 1977	129	1977
Signal detection via spectral theory of large dimensional random matrices JW Silverstein, PL Combettes IEEE Transactions on Signal Processing 40 (8), 2100-2105, 1992	103	1992
No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix D Paul, JW Silverstein Journal of Multivariate Analysis 100 (1), 37-57, 2009	100	2009
The random matrix regime of Maronna’s M-estimator with elliptically distributed samples R Couillet, F Pascal, JW Silverstein Journal of Multivariate Analysis 139, 56-78, 2015	88	2015
Weak convergence of random functions defined by the eigenvectors of sample covariance matrices JW Silverstein The Annals of Probability, 1174-1194, 1990	83	1990
Analysis of the limiting spectral distribution of large dimensional information-plus-noise type matrices RB Dozier, JW Silverstein Journal of Multivariate Analysis 98 (6), 1099-1122, 2007	76	2007

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