Spectral analysis of large dimensional random matrices Z Bai, JW Silverstein Springer, 2010 | 2073 | 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 | 1412 | 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 | 855 | 1995 |
Eigenvalues of large sample covariance matrices of spiked population models J Baik, JW Silverstein Journal of multivariate analysis 97 (6), 1382-1408, 2006 | 796 | 2006 |
CLT for linear spectral statistics of large-dimensional sample covariance matrices ZD Bai, JW Silverstein Advances In Statistics, 281-333, 2008 | 637 | 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 | 633 | 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 | 581 | 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 | 370 | 1995 |
The smallest eigenvalue of a large dimensional Wishart matrix JW Silverstein The Annals of Probability, 1364-1368, 1985 | 334 | 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 | 220 | 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 | 208 | 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 | 179 | 2007 |
Exact separation of eigenvalues of large dimensional sample covariance matrices ZD Bai, JW Silverstein Annals of probability, 1536-1555, 1999 | 173 | 1999 |
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 | 172 | 2010 |
Spectral analysis of networks with random topologies U Grenander, JW Silverstein SIAM Journal on Applied Mathematics 32 (2), 499-519, 1977 | 127 | 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 | 102 | 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 | 98 | 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 | 91 | 2015 |
Weak convergence of random functions defined by the eigenvectors of sample covariance matrices JW Silverstein The Annals of Probability, 1174-1194, 1990 | 82 | 1990 |
On the eigenvectors of large dimensional sample covariance matrices JW Silverstein Journal of multivariate analysis 30 (1), 1-16, 1989 | 76 | 1989 |