Exact Separation of Eigenvalues of Large Dimensional Sample Covariance Matrices
Bai, Z. D. ; Silverstein, Jack W.
Ann. Probab., Tome 27 (1999) no. 1, p. 1536-1555 / Harvested from Project Euclid
Let $B _n = (1/N) T_n^{1/2} X _n X _n^*T_n^{1/2}$ where $X_n$ is $n \times N$ with i.i.d. complex standardized entries having finite fourth moment, and $T_n^{1/2}$ is a Hermitian square root of the nonnegative definite Hermitian matrix $T_n$. It was shown in an earlier paper by the authors that, under certain conditions on the eigenvalues of $T_n$, with probability 1 no eigenvalues lie in any interval which is outside the support of the limiting empirical distribution (known to exist) for all large $n$. For these $n$ the interval corresponds to one that separates the eigenvalues of $T_n$. The aim of the present paper is to prove exact separation of eigenvalues; that is, with probability 1, the number of eigenvalues of $B_n$ and $T_n$ lying on one side of their respective intervals are identical for all large $n$.
Publié le : 1999-07-14
Classification:  Random matrix,  empirical distribution function of eigenvalues,  Stielt-jes transform,  15A52,  60F15,  62H99
@article{1022677458,
     author = {Bai, Z. D. and Silverstein, Jack W.},
     title = {Exact Separation of Eigenvalues of Large Dimensional Sample
		 Covariance Matrices},
     journal = {Ann. Probab.},
     volume = {27},
     number = {1},
     year = {1999},
     pages = { 1536-1555},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1022677458}
}
Bai, Z. D.; Silverstein, Jack W. Exact Separation of Eigenvalues of Large Dimensional Sample
		 Covariance Matrices. Ann. Probab., Tome 27 (1999) no. 1, pp.  1536-1555. http://gdmltest.u-ga.fr/item/1022677458/