| Spectral analysis of large dimensional random matrices Z Bai, JW Silverstein Springer, 2010 | 2174 | 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 | 1431 | 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 | 868 | 1995 |
| Eigenvalues of large sample covariance matrices of spiked population models J Baik, JW Silverstein Journal of multivariate analysis 97 (6), 1382-1408, 2006 | 823 | 2006 |
| CLT for linear spectral statistics of large-dimensional sample covariance matrices ZD Bai, JW Silverstein Advances In Statistics, 281-333, 2008 | 662 | 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 | 647 | 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 | 595 | 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 | 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 | 218 | 1988 |
| 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 | 182 | 2010 |
| 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 |
| 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 | 87 | 2015 |
| Weak convergence of random functions defined by the eigenvectors of sample covariance matrices JW Silverstein The Annals of Probability, 1174-1194, 1990 | 84 | 1990 |
| On the eigenvectors of large dimensional sample covariance matrices JW Silverstein Journal of multivariate analysis 30 (1), 1-16, 1989 | 77 | 1989 |
Romain COUILLETUGA IDEX DataScience Chair, GIPSA-lab, University Grenoble-Alpes在 gipsa-lab.grenoble-inp.fr 的电子邮件经过验证
