Limiting spectral distribution for a type of sample covariance matrices

被引：0
|
作者：
Junshan Xie
机构：
[1] Zhejiang University,Department of Mathematics
[2] Henan University,College of Mathematics and Information
来源：
Indian Journal of Pure and Applied Mathematics | 2013年 / 44卷
关键词：
Sample covariance matrices; empirical spectral distribution; isotropic log-concave random vector;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper focuses on the limiting spectral distribution of the sample covariance matrices with information plus noise type data, which is interesting in the area of array signal processing. Assume that the noise data comes from a multivariate population with an isotropic and log-concave probability law. It is shown that in probability, the empirical spectral distribution converges weakly to a non-random probability distribution, whose Stieltjes transform satisfies a certain equation.
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页码：695 / 710
页数：15
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