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Qinwen WANG
Qinwen WANG
fudan.edu.cn의 이메일 확인됨
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On the sphericity test with large-dimensional observations
Q Wang, J Yao
692013
Identifying the number of factors from singular values of a large sample auto-covariance matrix
Z Li, Q Wang, J Yao
452017
Extreme eigenvalues of large-dimensional spiked Fisher matrices with application
Q Wang, J Yao
342017
A note on the CLT of the LSS for sample covariance matrix from a spiked population model
Q Wang, JW Silverstein, J Yao
Journal of Multivariate Analysis 130, 194-207, 2014
252014
Central limit theorem for linear spectral statistics of large dimensional Kendall’s rank correlation matrices and its applications
Z Li, Q Wang, R Li
The Annals of Statistics 49 (3), 1569-1593, 2021
152021
Moment approach for singular values distribution of a large auto-covariance matrix
Q Wang, J Yao
112016
Joint CLT for several random sesquilinear forms with applications to large-dimensional spiked population models
W Qinwen, S Zhonggen, Y Jianfeng
72014
On eigenvalues of a high-dimensional spatial-sign covariance matrix
W Li, Q Wang, J Yao, W Zhou
Bernoulli 28 (1), 606-637, 2022
62022
Provable more data hurt in high dimensional least squares estimator
Z Li, C Xie, Q Wang
arXiv preprint arXiv:2008.06296, 2020
62020
On singular values distribution of a matrix large auto-covariance in the ultra-dimensional regime
Q Wang, J Yao
Random Matrices: Theory and Applications 4 (04), 1550015, 2015
62015
Asymptotic normality and confidence intervals for prediction risk of the min-norm least squares estimator
Z Li, C Xie, Q Wang
International Conference on Machine Learning, 6533-6542, 2021
32021
On eigenvalues of a high-dimensional Kendall’s rank correlation matrix with dependence
Z Li, C Wang, Q Wang
Science China Mathematics 66 (11), 2615-2640, 2023
22023
On eigenvalues of a high dimensional Kendall's rank correlation matrix with dependences
C Wang, Q Wang, Z Li
arXiv e-prints, arXiv: 2109.13624, 2021
12021
Eigenvalue distribution of a high-dimensional distance covariance matrix with application
W Li, Q Wang, J Yao
arXiv preprint arXiv:2105.07641, 2021
12021
Distance correlation test for high-dimensional independence
W LI, Q WANG, J YAO
A Technical tools
W Li, Q Wang, J Yao
Supplementary Materials for “Asymptotic Normality and Confidence Intervals for Prediction Risks of the Min-Norm Least Squares Estimator”
Z Li, C Xie, Q Wang
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학술자료 1–17