79 citations · 170 across the 10 of their papers we have counts for
10 papers
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…
Extreme eigenvalues of large-dimensional spiked Fisher matrices with application
Qinwen Wang, Jianfeng Yao
Consider two -variate populations, not necessarily Gaussian, with covariance matrices and , respectively, and let and be the sample covariances matrices f…
On singular values distribution of a large auto-covariance matrix in the ultra-dimensional regime
Qinwen Wang, Jianfeng Yao
Let be a sequence of independent real random vectors of -dimension and let be the lag- ($…
Moment approach for singular values distribution of a large auto-covariance matrix
Qinwen Wang, Jianfeng Yao
Let be a sequence of independent real random vectors of -dimension and let be the lag- (…
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…
Joint CLT for several random sesquilinear forms with applications to large-dimensional spiked population models
Qinwen Wang, Zhonggen Su, Jianfeng Yao
In this paper, we derive a joint central limit theorem for random vector whose components are function of random sesquilinear forms. This result is a natural extension of the exist…
A note on the CLT of the LSS for sample covariance matrix from a spiked population model
Qinwen Wang, Jack W. Silverstein, Jianfeng Yao
In this note, we establish an asymptotic expansion for the centering parameter appearing in the central limit theorems for linear spectral statistic of large-dimensional sample cov…
On the sphericity test with large-dimensional observations
Qinwen Wang, Jianfeng Yao
In this paper, we propose corrections to the likelihood ratio test and John's test for sphericity in large-dimensions. New formulas for the limiting parameters in the CLT for linea…