CLT for linear spectral statistics of normalized sample covariance matrices with the dimension much larger than the sample size

被引:13
作者
Chen, Binbin [1 ]
Pan, Guangming [1 ]
机构
[1] Nanyang Technol Univ, Sch Phys & Math Sci, Div Math Sci, Singapore 639798, Singapore
关键词
central limit theorem; empirical spectral distribution; hypothesis test; linear spectral statistics; sample covariance matrix; CONVERGENCE; TESTS; CLASSIFICATION;
D O I
10.3150/14-BEJ599
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 A = 1/root np ((XX)-X-T - pI(n)) where X is a p x n matrix, consisting of independent and identically distributed (i.i.d.) real random variables X-ij with mean zero and variance one. When p / n -> infinity, under fourth moment conditions a central limit theorem (CLT) for linear spectral statistics (LSS) of A defined by the eigenvalues is established. We also explore its applications in testing whether a population covariance matrix is an identity matrix.
引用
收藏
页码:1089 / 1133
页数:45
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