姓　　名：李卫明
职　　称：副教授
研究方向：高维统计分析，随机矩阵理论
教授课程：多元分析，时间序列分析
E - mail：li.weiming@sufe.edu.cn；电话：**


研究项目
序号 项目名称 项目编号 项目来源 起止时间 项目经费
1 基于随机矩阵理论的高维协方差矩阵的统计推断 ** 国家自然科学基金青年基金 2015/01-2017/12 22万

研究领域
高维统计分析，随机矩阵谱分析，秩集抽样，舍入误差分析


教育经历
2010年
东北师范大学，博士
2006年
东北师范大学，硕士
2002年
哈尔滨师范大学，学士

工作经历
2018/08-至今 上海财经大学，统计与管理学院，副教授?
2016/07-2018/07 上海财经大学，统计与管理学院，助教授
2013/03-2016/07 北京邮电大学，理学院，讲师
2011/03-2013/02 北京航空航天大学，数学与系统科学学院，博士后

研究成果
Working paper:
-- Hu, J., Li, W.M., Zhou, W. Central limit theorem for mutual information of large MIMO systems with elliptically correlated channels. Submitted.
2018+
-- Hu, J., Li, W.M., Liu Z., Zhou, W. High-dimensional covariance matrices in elliptical distributions with
application to spherical test. Ann. Stat., 47(1), 527-555, 2019.
-- Li, W.M., Li, Z., Yao, J.F. Joint CLT for dependent linear spectral statistics of large dimensional sample covariance matrices with applications.Scand. J. Stat.,45, 699-728, 2018.
-- Chen, J.Q., Zhang, Y.C., Li, W.M., Tian, B.P. A supplement on CLT for LSS under a large dimensional generalized spiked covariance model.Statistics & Probability Letters,138, 57-65, 2018.
-- Li, W.M. & Yao, J.F. On structure testing for component covariance matrices of a high-dimensional mixture.J. R. Statist. Soc. B，80，293–318，2018.
2016-2017:
-- Qin, Y.L. & Li, W.M. Bias-reduced estimators of moments of a population spectral distribution and their applications. In Big and Complex Data Analysis: Statistical Methodologies and Applications (Ejaz Ahmed ed.), Springer, 2017.
-- Li, W.M., Chen, J.Q., Yao, J.F. Testing the independence of two random vectors where only one dimension is large.Statistics,51, 141-153, 2017.
-- Qin, Y.L. & Li, W.M. Testing the order of a population spectral distribution for high-dimensional data.Computational Statistics & Data analysis, 95, 75-82, 2016.
-- Li, W.M. & Liu, Z. A test for the complete independence of high-dimensional random vectors.Journal of Statistical Computation and Simulation. 86,3135-3140,2016.
2014-2015:
-- Tian, X.T., Lu, Y.T. & Li, W.M. A robust test for sphericity of high dimensional covariance matrices.Journal of Multivariate Analysis, 141, 217-227, 2015.
-- Li, W.M. & Yao, J.F. On generalized expectation based estimation of a population spectral distribution from high-dimensional data.Annals of the Institute of Statistical Mathematics, 67, 359-373, 2015.
-- Li, W.M. & Qin, Y.L. Hypothesis testing for high-dimensional covariance matrices.Journal of Multivariate Analysis, 128, 108-119, 2014.
-- Li, W.M. & Yao, J.F. A local moment estimator of the spectrum of a large dimensional covariance matrix,Statistica Sinica, 24, 919-936, 2014.
-- Li, W.M. Local expectations of the population spectral distribution of a high-dimensional covariance matrix,Statistical Papers, 55, 563-573, 2014.
2011-2013:
-- Li, W.M., Chen, J.Q., Qin, Y.L., Yao, J.F. & Bai, Z.D. Estimation of the population spectral distribution from a large dimensional sample covariance matrix,Journal of Statistical Planning and Inference, 143, 1887-1897, 2013.
-- Li, W.M., Liu, T.Q. & Bai, Z.D. Rounded data analysis based on ranked set sample,Statistical Papers, 53, 439-455, 2012.
-- Li, W.M. & Bai, Z.D. Analysis of accumulated rounding errors in autoregressive processes,Journal of Time Series Analysis, 32, 518-530, 2011.
-- Li, W.M. & Bai, Z.D. Rounded data analysis based on multi-layer ranked set sampling,Acta Mathematica Sinica (English Series),27, 2507-2518, 2011.


奖励，荣誉

社会工作
Annals of Statistics, Bernoulli, Journal of Multivariate analysis 等杂志审稿人


学术报告（2008年以来）
-- 2017/07 The31st European Meeting of Statisticians, Helsinki, Invited talk,On structure testing for component covariance matrices of a high-dimensional mixture.
-- 2016/12 The 10thICSA international conference, China, Shanghai, Invited talk, On an example where the MP law does not hold.
-- 2014/07 The 3rd Institute of Mathematical Statistics APRM, Tai Wan, Contributed talk, Hypothesis testing for high-dimensional covariance matrices.
-- 2013/08 The 59th World Statistics Congress, Hong Kong, Invited talk,On generalized expectation based estimation of a population spectral distribution from high-dimensional data.