Name: Li Weiming
Title: Professor
Research Direction: High-dimensional statistical analysis, Random matrix theory
Courses Taught: Multivariate analysis, Time series analysis
E-mail: li.weiming@sufe.edu.cn; Phone: 65901019
研究项目
Serial Number | Project Name | Project Number | Project Source | Start and End Time | Project funding |
1 | Statistical Inference of High-Dimensional Covariance Matrices Based on Random Matrix Theory | 11401037 | National Natural Science Foundation of China Youth Fund | 2015/01-2017/12 | 220,000 |
2 | Limit Properties of Several Spectral Statistics under the Multivariate Elliptical Distribution and Their Applications | 11971293 | National Natural Science Foundation General Fund | 2020/01-2023/12 | 520,000 |
3 | New Wireless Communication Mathematical Theory and Mathematical Techniques Assisted by Intelligent Reflective Surfaces | 12141107 | National Natural Science Foundation Special Fund (Subproject Leader) | 2022/01-2025/12 | 490,000 |
4 | Statistical Inference Based on Random Matrix Theory Under the Ultra-High Dimensional Asymptotic Framework | 12471261 | National Natural Science Foundation General Fund | 2025/01-2028/12 | 430,000 |
Research Field
High-dimensional statistical analysis, random matrix spectral analysis, rank set sampling, rounding error analysis
Education Experience
2010 Northeast Normal University, Ph.D.
2006 Northeast Normal University, Master's
2002 Harbin Normal University, Bachelor
Work Experience
2025/07-Present Shanghai University of Finance and Economics, School of Statistics and Data Science, Professor
2018/08-2025/07 Shanghai University of Finance and Economics, School of Statistics and Data Science, Associate Professor
2016/07-2018/07 Shanghai University of Finance and Economics, School of Statistics and Data Science, Assistant Professor
2013/03-2016/07 Beijing University of Posts and Telecommunications, School of Science, Lecturer
2011/03-2013/02 Beihang University, School of Mathematics and Systems Science, Postdoctoral Fellow
Research Achievements
Research Paper
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.
International Conference
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
8.Li, W.M., On generalized expectation based estimation of a population spectral distribution from high-dimensional data. The 59th World Statistics Congress, Hong Kong, 2013.8.25-30.
Social Work
2020--Present, Associate Editor of CSDA
2025--Currently, Vice Chairman of the Random Matrix Theory and Applications Branch of the Chinese Society for Field Statistics
Reviewers for journals such as AOS, JASA, AAP, etc.
