4 citations · 11 across the 18 of their papers we have counts for
18 papers
Local Laws and Edge Universality for Noncentral Sample Covariance Matrices
Can Hu, Jiang Hu, Zhidong Bai
We consider the real noncentral sample covariance matrices with . Here is deterministic, $Σ\in \mathbb{R}^{M\times…
Functional CLT for general sample covariance matrices
Jian Cui, Zhijun Liu, Jiang Hu +1
This paper studies the central limit theorems (CLTs) for linear spectral statistics (LSSs) of general sample covariance matrices, when the test functions belong to , the class…
Asymptotics for Reinforced Stochastic Processes on Hierarchical Networks
Li Yang, Dandan Jiang, Jiang Hu +1
In this paper, we analyze the asymptotic behavior of a system of interacting reinforced stochastic processes on a directed network of agents. The sys…
A general partial Cramér's condition for Edgeworth expansion of a function of sample means with applications
Yashi Wei, Jiang Hu, Zhidong Bai
A large class of statistics can be formulated as smooth functions of sample means of random vectors. In this paper, we propose a general partial Cramér's condition (GPCC) and apply…
Asymptotic distributions of four linear hypotheses test statistics under generalized spiked model
Zhijun Liu, Jiang Hu, Zhidong Bai +1
In this paper, we establish the Central Limit Theorem (CLT) for linear spectral statistics (LSSs) of large-dimensional generalized spiked sample covariance matrices, where the spik…
Edgeworth corrections for the spiked eigenvalues of non-Gaussian sample covariance matrices with applications
Yashi Wei, Jiang Hu, Zhidong Bai
Yang and Johnstone (2018) established an Edgeworth correction for the largest sample eigenvalue in a spiked covariance model under the assumption of Gaussian observations, leaving…
On the rate of convergence in the CLT for LSS of large-dimensional sample covariance matrices
Jian Cui, Jiang Hu, Zhidong Bai +1
This paper investigates the rate of convergence for the central limit theorem of linear spectral statistic (LSS) associated with large-dimensional sample covariance matrices. We co…
Spectrally-Corrected and Regularized QDA Classifier for Spiked Covariance Model
Wenya Luo, Hua Li, Zhidong Bai +1
Quadratic discriminant analysis (QDA) is a widely used method for classification problems, particularly preferable over Linear Discriminant Analysis (LDA) for heterogeneous data. H…
A revisit of the circular law
Zhidong Bai, Jiang Hu
Consider a complex random matrix , whose entries are independent random variables with zero means and unit variances. It is wel…
Asymptotic properties of a multicolored random reinforced urn model with an application to multi-armed bandits
Li Yang, Jiang Hu, Jianghao Li +1
The random self-reinforcement mechanism, characterized by the principle of ``the rich get richer'', has demonstrated significant utility across various domains. One prominent model…
The Asymptotic Properties of the Extreme Eigenvectors of High-dimensional Generalized Spiked Covariance Model
Zhangni Pu, Xiaozhuo Zhang, Jiang Hu +1
In this paper, we investigate the asymptotic behaviors of the extreme eigenvectors in a general spiked covariance matrix, where the dimension and sample size increase proportionall…
Test for high-dimensional linear hypothesis of mean vectors via random integration
Jianghao Li, Shizhe Hong, Zhenzhen Niu +1
In this paper, we investigate hypothesis testing for the linear combination of mean vectors across multiple populations through the method of random integration. We have establishe…
Simultaneous test of the mean vectors and covariance matrices for high-dimensional data using RMT
Zhenzhen Niu, Jianghao Li, Wenya Luo +1
In this paper, we propose a new modified likelihood ratio test (LRT) for simultaneously testing mean vectors and covariance matrices of two-sample populations in high-dimensional s…
Revised BDS Test
Wenya Luo, Zhidong Bai, Jiang Hu +1
In this paper, we focus on the BDS test, which is a nonparametric test of independence. Specifically, the null hypothesis of it is that is i.i.d. (independent a…
Test for high-dimensional mean vectors via the weighted -norm
Jianghao Li, Zhenzhen Niu, Shizhe Hong +1
In this paper, we propose a novel approach to test the equality of high-dimensional mean vectors of several populations via the weighted -norm. We establish the asymptotic nor…
Central limit theorem for linear spectral statistics of large dimensional separable sample covariance matrices
Bai Zhidong, Li Huiqin, Pan Guangming
Suppose that is whose elements are independent real variables with mean zero, variance 1 and the fourth moment equal to three. The separable samp…
Strong limit of the extreme eigenvalues of a symmetrized auto-cross covariance matrix
Chen Wang, Baisuo Jin, Z. D. Bai +2
The auto-cross covariance matrix is defined as \[\mathbf{M}_n=\frac{1} {2T}\sum_{j=1}^T\bigl(\mathbf{e}_j\mathbf{e}_{j+τ}^*+\mathbf{e}_{j+ τ}\mathbf{e}_j^*\bigr),\] where $\mathbf{…
Convergence of Empirical Spectral Distributions of Large Dimensional Quaternion Sample Covariance Matrices
Huiqin Li, Zhidong Bai, Jiang Hu
In this paper we establish the limit of the empirical spectral distribution of quaternion sample covariance matrices. Suppose is a quat…