185 citations · 363 across the 16 of their papers we have counts for
7 papers · 1 filter
Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices
Zhixiang Zhang, Shurong Zheng, Guangming Pan +1
We consider general high-dimensional spiked sample covariance models and show that their leading sample spiked eigenvalues and their linear spectral statistics are asymptotically i…
Community Detection Based on the convergence of eigenvectors in DCBM
Yan Liu, Zhiqiang Hou, Zhigang Yao +3
Spectral clustering is one of the most popular algorithms for community detection in network analysis. Based on this rationale, in this paper we give the convergence rate of eigenv…
Central limit theorem for linear spectral statistics of general separable sample covariance matrices with applications
Huiqin Li, Yanqing Yin, Shurong Zheng
In this paper, we consider the separable covariance model, which plays an important role in wireless communications and spatio-temporal statistics and describes a process where the…
CLT for large dimensional general Fisher matrices and its applications in high-dimensional data analysis
Shurong Zheng, Zhidong Bai, Jianfeng Yao
Random Fisher matrices arise naturally in multivariate statistical analysis and understanding the properties of its eigenvalues is of primary importance for many hypothesis testing…
CLT for linear spectral statistics of random matrix
Shurong Zheng, Zhidong Bai, Jianfeng Yao
This paper proposes a CLT for linear spectral statistics of random matrix for a general non-negative definite and {\bf non-random} Hermitian matrix .
A Note on Central Limit Theorems for Linear Spectral Statistics of Large Dimensional F-matrix
Shurong Zheng, Zhidong Bai
Sample covariance matrix and multivariate -matrix play important roles in multivariate statistical analysis. The central limit theorems {\sl (CLT)} of linear spectral statistics…
Corrections to LRT on Large Dimensional Covariance Matrix by RMT
Zhidong Bai, Dandan Jiang, Jian-feng Yao +1
In this paper, we give an explanation to the failure of two likelihood ratio procedures for testing about covariance matrices from Gaussian populations when the dimension is large…