185 citations · 363 across the 16 of their papers we have counts for
16 papers
Adaptive Tests for Bandedness of High-dimensional Covariance Matrices
Xiaoyi Wang, Gongjun Xu, Shurong Zheng
Estimation of the high-dimensional banded covariance matrix is widely used in multivariate statistical analysis. To ensure the validity of estimation, we aim to test the hypothesis…
On block-wise and reference panel-based estimators for genetic data prediction in high dimensions
Bingxin Zhao, Shurong Zheng, Hongtu Zhu
Genetic prediction of complex traits and diseases has attracted enormous attention in precision medicine, mainly because it has the potential to translate discoveries from genome-w…
Two-directional simultaneous inference for high-dimensional models
Wei Liu, Huazhen Lin, Jin Liu +1
This paper proposes a general two directional simultaneous inference (TOSI) framework for high-dimensional models with a manifest variable or latent variable structure, for example…
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…
Estimating Number of Factors by Adjusted Eigenvalues Thresholding
Jianqing Fan, Jianhua Guo, Shurong Zheng
Determining the number of common factors is an important and practical topic in high dimensional factor models. The existing literatures are mainly based on the eigenvalues of the…
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 linear spectral statistics of large dimensional sample covariance matrices with dependent data
Shurong Zheng, Zhidong Bai, Jianfeng Yao +1
This paper investigates the central limit theorem for linear spectral statistics of high dimensional sample covariance matrices of the form $\mathbf{B}_n=n^{-1}\sum_{j=1}^{n}\mathb…
A New Test of Multivariate Nonlinear Causality
Zhidong Bai, Yongchang Hui, Zhihui Lv +3
The multivariate nonlinear Granger causality developed by Bai et al. (2010) plays an important role in detecting the dynamic interrelationships between two groups of variables. Fol…
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…
Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing
Shurong Zheng, Z. D. Bai, Jiangfeng Yao
Sample covariance matrices are widely used in multivariate statistical analysis. The central limit theorems (CLT's) for linear spectral statistics of high-dimensional non-centered…
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…
Testing linear hypotheses in high-dimensional regressions
Z. Bai, D. Jiang, J. Yao +1
For a multivariate linear model, Wilk's likelihood ratio test (LRT) constitutes one of the cornerstone tools. However, the computation of its quantiles under the null or the altern…
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…
Variable Selection Incorporating Prior Constraint Information into Lasso
Shurong Zheng, Guodong Song, Ning-Zhong Shi
We propose the variable selection procedure incorporating prior constraint information into lasso. The proposed procedure combines the sample and prior information, and selects sig…