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20132019
most citedTesting the independence of two random vectors where only one dimension is large

11 citations · 22 across the 8 of their papers we have counts for

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8 papers

math.ST2019★ 1 cited

On eigenvalues of a high-dimensional spatial-sign covariance matrix

Weiming Li, Qinwen Wang, Jianfeng Yao +1

This paper investigates limiting properties of eigenvalues of multivariate sample spatial-sign covariance matrices when both the number of variables and the sample size grow to inf…

math.ST2018★ 4 cited

High-dimensional covariance matrices in elliptical distributions with application to spherical test

Jiang Hu, Weiming Li, Zhi Liu +1

This paper discusses fluctuations of linear spectral statistics of high-dimensional sample covariance matrices when the underlying population follows an elliptical distribution. Su…

math.ST2018★ 1 cited

Joint CLT for eigenvalue statistics from several dependent large dimensional sample covariance matrices with application

Weiming Li, Zeng Li, Jianfeng Yao

Let be a data matrix with complex-valued, independent and standardized entries satisfying a Lindeberg-type moment condition. We consider simult…

math.ST2017

On spectral properties of high-dimensional spatial-sign covariance matrices in elliptical distributions with applications

Weiming Li, Wang Zhou

Spatial-sign covariance matrix (SSCM) is an important substitute of sample covariance matrix (SCM) in robust statistics. This paper investigates the SSCM on its asymptotic spectral…

stat.ME2017★ 1 cited

On structure testing for component covariance matrices of a high-dimensional mixture

Weiming Li, Jianfeng Yao

By studying the family of -dimensional scale mixtures, this paper shows for the first time a non trivial example where the eigenvalue distribution of the corresponding sample co…

stat.ME2015★ 11 cited

Testing the independence of two random vectors where only one dimension is large

Weiming Li, Jiaqi Chen, Jianfeng Yao

For testing the independence of two vectors with respective dimensions and , the existing literature in high-dimensional statistics all assume that both dimensions …