Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices

被引:312
|
作者
Silverstein, JW
机构
关键词
random matrix; empirical distribution function of eigenvalues; Stieltjes transform;
D O I
10.1006/jmva.1995.1083
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Let X be n x N containing i.i.d. complex entries with E \X(11) - EX(11)\(2)=1, and T an n x n random Hermitian nonnegative definite, independent ofX. Assume, almost surely, as n --> infinity, the empirical distribution function (e.d.E) of the eigenvalues or. T converges in distribution, and the ratio n/N tends to a positive number. Then it is shown that, almost surely, the e.d.f. of the eigenvalues of (1/N) XX*T converges in distribution. The limit is nonrandom and is characterized in terms of its Stieltjes transform, which satisfies a certain equation. (C) 1995 Academic Press, Inc.
引用
收藏
页码:331 / 339
页数:9
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