李卫明

高维统计分析，随机矩阵谱分析，秩集抽样，舍入误差分析

科研论文

1.Hong, S.Z., Li, W.M., Liu, Q., and Zhang, Y.C. (2025). An adaptive adjustment to the R2 statistic in high-dimensional elliptical models. Journal of the American Statistical Association. Doi: 10.1080/01621459.2024.2448859.

2.Li, R.Z.,Li, W.M., and Wang, Q.W. (2025). Tests for shape matrices in moderated dimension via Tyler's M estimators. Journal of the American Statistical Association.120 (549), 472-485.

3.Zhang,Q.Y., Bai, Z.D., and Li, W.M. (2025). Two-sample Behrens–Fisher problems for high-dimensional data: A ridgelized Wald-type test. Random Matrices: Theory and Applications.14(2), 2550005.

4.Li, W.M., Yao, J.F., and Zheng, S.R. (2024). Chapter 5 - random matrix theory and high-dimensional statistics. In Srinivasa Rao, A.S., Bai, Z., and Rao, C.,editors, Probability Models, volume 51 of Handbook of Statistics, pages 119-141. Elsevier.

5.Li, W.M. and Hong, S.Z. (2024). CLT for high-dimensional R2 statistics under a general independent components model. Statistica Sinica. 34, 2265-2275.

6.Li, W.M., Wang, Q.W., and Yao, J.F. (2024). Distance correlation test for high-dimensional independence. Bernoulli, 30(4): 3165-3192.

7.Li, W.M. and Xiong, X.G. (2023). A test for the identity of a high-dimensional correlation matrix based on the ℓ4-norm. Journal of Statistical Planning and Inference, 225, 132-145.

8.Li, W.M.nd Zhu, J.P. (2023). CLT for spiked eigenvalues of a sample covariance matrix from high-dimensional Gaussian mean mixtures. Journal of Multivariate Analysis, 197, 105127.

9.Li, W.M.,Wang, Q.W., and Yao, J.F. (2023). Eigenvalue distribution of a high-dimensional distance covariance matrix with application. Statistica Sinica, 33, 149-168.

10.Li, W.M.,Wang, Q.W., Yao, J.F., and Zhou, W. (2022). On eigenvalues of a high-dimensional spatial-sign covariance matrix. Bernoulli, 28 (1),606–637.

11.Zhang,Y.C. Hu, J. and Li, W.M. (2022). CLT for linear spectral statistics of high-dimensional sample covariance matrices in elliptical distributions. Journal of Multivariate Analysis, 191, 105007.

12.Li, W.M.and Xu, Y.C. (2022). Asymptotic properties of high-dimensional spatial median in elliptical distributions with application. Journal of Multivariate Analysis, 190, 104975.

13.Hu, J.,Li, W.M. and Zhou, W. (2019). Central limit theorem for mutual information of large MIMO systems with elliptically correlated channels. IEEE Transactions on Information Theory. 65 (11), 7168-7180.

14.Hu, J., Li, W.M., Liu Z. and Zhou, W. (2019). High-dimensional covariance matrices in elliptical distributions with application to spherical test.Annals of Statistics,47(1), 527-555.

15.Li, W.M., Li, Z. and Yao, J.F. (2018). Joint CLT for dependent linear spectral statistics of large dimensional sample covariance matrices with applications. Scandinavian Journal of Statistics, 45, 699-728.

16.Li, W.M. and Yao, J.F. On structure testing for component covariance matrices of a high-dimensional mixture. Journal of the Royal Statistical Society, Series B, 80, 293–318，2018.

17.Chen,J.Q., Zhang, Y.C., Li, W.M. and Tian, B.P. (2018). A supplement on CLT for LSS under a large dimensional generalized spiked covariance model.Statistics & Probability Letters,138, 57-65.

18.Li, W.M.,Chen, J.Q. and Yao, J.F. (2017). Testing the independence of two random vectors where only one dimension is large. Statistics, 51,141-153.

19.Qin, Y.L.and Li, W.M. (2017) Bias-reduced estimators of moments of a population spectral distribution and their applications. In Big and Complex Data Analysis:StatisticalMethodologies and Applications (Ejaz Ahmeded.), Springer.

20.Li, W.M.nd Liu, Z. (2016). A test for the complete independence of high-dimensional random vectors. Journal of Statistical Computation and Simulation. 86, 3135-3140.

21.Qin, Y.L.and Li, W.M. (2016). Testing the order of a population spectral distribution for high-dimensional data. Computational Statistics & Data analysis, 95, 75-82.

22.Tian,X.T., Lu, Y.T. and Li, W.M. (2015). A robust test for sphericity of high dimensional covariance matrices. Journal of Multivariate Analysis, 141, 217-227.

23.Li, W.M.and Yao, J.F. (2015). On generalized expectation based estimation of a population spectral distribution from high-dimensional data. Annals of the Institute of Statistical Mathematics,67, 359-373.

24.Li, W.M.and Qin, Y.L. (2014). Hypothesis testing for high-dimensional covariance matrices. Journal of Multivariate Analysis,128, 108-119.

25.Li, W.M.and Yao, J.F. (2014). A local moment estimator of the spectrum of a large dimensional covariance matrix, Statistica Sinica, 24, 919-936.

26.Li,W.M. (2014). Local expectations of the population spectral distribution of a high-dimensional covariance matrix, Statistical Papers, 55, 563-573.

27.Li, W.M.,Chen, J.Q., Qin, Y.L., Yao, J.F. and Bai, Z.D. (2013). Estimation of the population spectral distribution from a large dimensional sample covariance matrix, Journal of Statistical Planning and Inference, 143,1887-1897.

28.Li, W.M., Liu, T.Q.and Bai, Z.D. (2012).Rounded data analysis based on ranked set sample, Statistical Papers, 53, 439-455.

29.Li,W.M. and Bai, Z.D. (2011).Analysis of accumulated rounding errors in autoregressive processes,Journal of Time Series Analysis, 32, 518-530.

30.Li,W.M. and Bai, Z.D. (2011). Rounded data analysis based on multi-layer ranked set sampling, Acta Mathematica Sinica (English Series),27, 2507-2518.

国际会议

1.Li, W.M., A new adaptive adjustment to R^2 in high dimensions, Joint Statistical Meetings, Toronto, CA, 2023. 8.5-10.

2.Li, W.M., Asymptotics of spatial-sign based estimators of location and scatter in high-dimensions, 14th International Conference of the ERCIM WG on Computational and Methodological Statistics, King's College London, UK, 2021.12.18-20.

3.Bai, Z.D., Feng, X.D., Li, W.M., and Yao, J.F. Random Matrices and Complex Data Analysis Workshop. Shanghai University of Finance and Economics, 2019. 12. 09-12.

4.Li, W.M., On structure testing for component covariance matrices of a high-dimensional mixture, The 31st European Meeting of Statisticians, The University of Helsinki, 2017.7.24-28

5.Li, W.M., On structure testing for component covariance matrices of a high-dimensional mixture, 1st International Conference on Econometrics and Statistics, Hongkong University of Science and Technology, 2017.6.15-17

6.Li, W.M., On an example where the MP law does not hold, The 10th ICSA international conference, Shanghai Jiao Tong University, 2016.12.19-22

7.Li, W.M., Hypothesis testing for high-dimensional covariance matrices, The 3rd Institute of Mathematical Statistics APRM, National Taiwan University, 2014.6.29-7.3

2020--今，CSDA副主编

2025--今，中国现场统计研究会随机矩阵理论与应用分会副理事长

AOS、JASA、AAP等期刊审稿人