42 citations · 65 across the 12 of their papers we have counts for
13 papers
On Eigenvector Statistics of Large-Dimensional Sample Covariance Matrices with Applications
Weiming Li, Zeng Li, Qinwen Wang
We study the eigenvector-weighted spectral statistics \[ T_n(φ,g)=p^{-1}tr\{φ(\boldsymbol{S}_n)g(\boldsymbolΣ)\} \] of large-dimensional sample covariance matrices \(\boldsymbol{S}…
Mean-Shift PCA by Knockoff Mean
Mengda Li, Zeng Li, Jianfeng Yao
Removing noise is difficult, but adding noise is easy. In this work, we show how to eliminate mean-shift noisy components from PCA by deliberately introducing knockoff mean-shift p…
Robust estimation for number of factors in high dimensional factor modeling via Spearman correlation matrix
Jiaxin Qiu, Zeng Li, Jianfeng Yao
Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this paper, we introduce a new est…
On singular values of large dimensional lag-tau sample autocorrelation matrices
Zhanting Long, Zeng Li, Ruitao Lin
We study the limiting behavior of singular values of a lag- sample auto-correlation matrix of error term in the high-dimensional factor model. We establish the…
Asymptotic normality for eigenvalue statistics of a general sample covariance matrix when and applications
Jiaxin Qiu, Zeng Li, Jianfeng Yao
The asymptotic normality for a large family of eigenvalue statistics of a general sample covariance matrix is derived under the ultra-high dimensional setting, that is, when the di…
On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence
Zeng Li, Cheng Wang, Qinwen Wang
This paper investigates limiting spectral distribution of a high-dimensional Kendall's rank correlation matrix. The underlying population is allowed to have general dependence stru…
Provable More Data Hurt in High Dimensional Least Squares Estimator
Zeng Li, Chuanlong Xie, Qinwen Wang
This paper investigates the finite-sample prediction risk of the high-dimensional least squares estimator. We derive the central limit theorem for the prediction risk when both the…
Central Limit Theorem for Linear Spectral Statistics of Large Dimensional Kendall's Rank Correlation Matrices and its Applications
Zeng Li, Qinwen Wang, Runze Li
This paper is concerned with the limiting spectral behaviors of large dimensional Kendall's rank correlation matrices generated by samples with independent and continuous component…
Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model
Zeng Li, Fang Han, Jianfeng Yao
This paper studies the joint limiting behavior of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model, where the asymptotic reg…
On testing for high-dimensional white noise
Zeng Li, Clifford Lam, Jianfeng Yao +1
Testing for white noise is a classical yet important problem in statistics, especially for diagnostic checks in time series modeling and linear regression. For high-dimensional tim…
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…
Identifying the number of factors from singular values of a large sample auto-covariance matrix
Zeng Li, Qinwen Wang, Jianfeng Yao
Identifying the number of factors in a high-dimensional factor model has attracted much attention in recent years and a general solution to the problem is still lacking. A promisin…
On singular value distribution of large dimensional auto-covariance matrices
Zeng Li, Guangming Pan, Jianfeng Yao
Let be a sequence of independent dimensional random vectors and a given integer. From a sample $\varepsilon_1,\cdots,\varepsilon_{T+τ-1},\v…