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北京工业大学数理学院统计学导师介绍：李高荣
基本情况
李高荣，理学博士，博士后，副教授，2013年破格为博士生导师，美国数学评论(Mathematical Reviews)评论员。研究领域涉及非参数统计、复杂高维数据分析、经验似然推断、模型和变量选择、以及纵向数据和面板数据分析等。近年多次访问香港浸会大学和新加坡南洋理工大学，目前在国内外学术刊物 “The Annals of Statistics”、“Statistics and Computing”、“Statistica Sinica”、“Journal of Multivariate Analysis”、“Computational Statistics and Data Analysis”等上发表学术论文50多篇。曾获得中国博士后科学基金和上海市博士后科研资助计划项目。参加国家自然科学基金、北京市自然科学基金、高等学校博士学科点专项科研基金等多项科研项目。2010年入选北京市属高等学校人才强教深化计划“中青年骨干人才培养计划”和北京市优秀人才培养资助计划，2012年破格入选北京工业大学“京华人才”支持计划。目前主持国家自然科学基金、北京市自然科学基金、高等学校博士学科点专项科研基金和北京市教委科技计划面上项目。2011年指导美国数学建模竞赛(MCM)获一等奖(Meritorious)。
Address and contact
College of Applied Sciences
北京市朝阳区平乐园100号
Beijing University of Technology
北京工业大学应用数理学院
No.100 Pingle Yuan, Beijing 100124
邮编：100124
Office：
Mathematics and Physics Building 2414 (数理楼2414)
Email：
ligaorong@bjut.edu.cn; ligaorong@gmail.com
Experience
2009-present
Associate Professor, Beijing University of Technology
2007-2009
Post-doctoral Research Fellow, East China NormalUniversity
Education
Ph.D. in Probability and Mathematical Statistics (2007), Beijing University of Technology
M.S. in Applied Mathematics (2004), Hebei University of Technology, Tianjin
B.S. in Mathematics Education (1999), Shanxi Normal University, Linfen,Shanxi
Research Interests
Nonparametric Regression Methods
Semiparametric/Nonparametric Models
Complex and High Dimensional Data Analysis
Empirical Likelihood
Model/Variable Selection
Longitudinal/Panel Data Analysis
Grants (Principal Investigator)
☆The Beijing Natural Science Foundation (No: 1142002)
☆The National Nature Science Foundation of China (No: 11101014)
☆The Specialized Research Fund for the Doctoral Program of Higher Education of China (No: 20101103120016)
☆The Science and Technology Project of Beijing Municipal Education Commission (No: KM201410005010)
☆Program for JingHua Talents in Beijing University of Technology
☆Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of BeijingMunicipality (No: PHR20110822)
☆Training Programme Foundation for the Beijing Municipal Excellent Talents (No: 2010D005015000002)
☆The Fundamental Research Foundation of Beijing University of Technology (No: X4006013201101)
☆China Postdoctoral Science Foundation Funded Project (No: 20080430633)
☆Shanghai Postdoctoral Scientific Program (No: 08R214121)
Selected Publications
Li G. R., Lai P. and Lian H. (2014). Variable Selection and Estimation for Partially Linear Single-index Models with Longitudinal Data. Statistics and Computing (doi: 10.1007/s11222-013-9447-8, in press).
Li G. R., Peng H., Dong K. and Tong T. J. (2014). Simultaneous Confidence Bands and Hypothesis Testing in Single-index Models. Statistica Sinica, 24: 937-955.
Lian H. and Li G. R. (2014). Series Expansion for Functional Sufficient Dimension Reduction.Journal of Multivariate Analysis, 124: 150-165.
Yang Y. P., Li G. R. and Peng H. (2014). Empirical Likelihood for Varying Coefficient Errors-in-Variables Models with Longitudinal Data. Journal of Multivariate Analysis, 127: 1-18.
Yang Y. P., Li G. R.* and Lian H. (2014). Nonconcave Penalized Estimation in Partially Linear Models with Longitudinal Data. (To appear in Statistics).
Yang S. G., Xue L. G. and Li G. R. (2014). Simultaneous Confidence Bands for Single-index Random Effects Models with Longitudinal Data. Statistics and Probability Letters, 85: 6-14.
Li G. R., Lian H., Lai P. and Peng H. (2014). Variable Selection for Fixed Effects Varying Coefficient Models. (To appear in Acta Mathematica Sinica, English Series).
Yang X. J., Yang S. G. and Li G. R.* (2014). Simultaneous Confidence Band for Partially Linear Panel Data Models with Fixed Effects. (To appear in Acta Mathematicae Applicatae Sinica, English Series).
Feng S. Y., Li G. R.* and Zhang J. (2014). Efficient Statistical Inference for Partially Nonlinear Errors-in-Variables Models. Acta Mathematica Sinica, English Series, in press.
Li G. R., Lian H., Feng S. and Zhu L. X. (2013). Automatic Variable Selection for Longitudinal Generalized Linear Models. Computational Statistics and Data Analysis, 61: 174-186. (SCI: 098ME)
Lai P., Li G. R. and Lian H. (2013). Quadratic Inference Functions for Partially Linear Single-Index Models with Longitudinal Data. Journal of Multivariate Analysis, 118: 115-127. (SCI: 154ZD)
Li G. R., Peng H. and Tong T. J. (2013). Simultaneous Confidence Band for Nonparametric Fixed-Effects Panel Data Models. Economics Letters, 119: 229-232. (SSCI: 148IS)
Lai P., Li G. R. and Lian H. (2013). Semiparametric Estimation of Fixed Effects Panel Data Single-index Model. Statistics and Probability Letters, 83: 1595-1602. (SCI: 136TW)
Zhao P. X. and Li G. R. (2013). Modified SEE Variable Selection for Varying Coefficient Instrumental Variable Models. Statistical Methodology, 12: 60-70. (SCI: 115XD)
Li G. R., Peng H., Zhang J. and Zhu L. X. (2012). Robust Rank Correlation Based Screening.Annals of Statistics, 40(3): 1846-1877. (SCI: 031OG)
Li G. R., Xue L. G. and Lian H. (2012). SCAD-penalized Generalized Additive Models with NP-dimensionality. Journal of Nonparametric Statistics, 24(3): 681-697. (SCI: 994NB)
Li G. R., Lin L. and Zhu L. X. (2012). Empirical Likelihood for Varying Coefficient Partially Linear Model with Diverging Number of Parameters. Journal of Multivariate Analysis, 105: 85-111. (SCI: 887ED)
Zhang J., Feng S. Y., Li G. R.* and Lian H. (2011). Empirical Likelihood Inference for Partially Linear Panel Data Models with Fixed Effects. Economics Letters, 113: 165-167. (SSCI: 852ZS).
Li G. R., Xue L. G. and Lian H. (2011). Semi-varying Coefficient Models with a Diverging Number of Components. Journal of Multivariate Analysis, 102: 1166-1174. (SCI: 776FQ)
Zhang W., Li G. R.* and Xue L. G. (2011). Profile Inference on Partially Linear Varying-Coefficient EV Models under Restricted Condition. Computational Statistics and Data Analysis, 55: 3027-3040. (SCI: 799WS)
Li G. R., Peng H. and Zhu L. X. (2011). Nonconcave Penalized M-estimation with Diverging Number of Parameters. Statistica Sinica， 21: 391-419. (SCI：722LX).
Li G. R., Feng S. Y. and Peng H. (2011). Profile-type Smoothed Score Function for a Varying Coefficient Partially Linear Model. Journal of Multivariate Analysis, 102: 372-385. (SCI：690DV).
Li G. R., Zhu L. P. and Zhu L. X. (2010). Adaptive Confidence Region for the Direction in Semiparametric Regressions. Journal of Multivariate Analysis, 101: 1364-1377. (SCI: 578HP).
Li G. R., Zhu L. X., Xue L. G. and Feng S. Y. (2010). Empirical Likelihood Inference in Partially Linear Single-index Models for Longitudinal Data. Journal of Multivariate Analysis, 101: 718-732. (SCI: 546TM).
Zhu L. X., Lin L., Cui X. and Li G. R. (2010). Bias-corrected Empirical Likelihood in a Multi-link Semiparametric Model. Journal of Multivariate Analysis, 101: 850-868. (SCI: 551NU).
Wang X. L., Li G. R. and Lin L. (2011). Empirical Likelihood Inference for Semi-parametric Varying-coefficient Partially Linear Errors-in-variables Models. Metrika, 73(2): 171-185. (SCI: 709UN).
Li G. R., Chen S. and Zhang J. (2009). A class of Random Deviation Theorems and the Approach of Laplace Transform. Statistics & Probability Letters，79: 202-210. (SCI: 395VP).
Li G. R., Tian P. and Xue L. G. (2008). Generalized Empirical Likelihood Inference in Semiparametric Regression Model for Longitudinal Data. Acta Mathematica Sinica, English Series，24(12): 2029-2040. (SCI: 367JX).
Li G. R. and Xue L. G. (2008). Empirical Likelihood Confidence Region of the Parameter in the Partially Linear Errors-in-variables Model. Communications in Statistics -- Theory and Methods，37(10): 1552-1564. (SCI：278PG，EI: 20081411183009).
Li G. R., Chen S. and Feng S. Y. (2008). A Strong Limit Theorem for Functions of Continuous Random Variables and an Extension of the Shannon-McMillan Theorem. Journal of Applied Mathematics, 1-10. (SCI: V16EB, EI: 11405962)
Referee Service
Referee for more than 20 Journals: such as Journal of the American Statistical Association、Scandinavian Journal of Statistics、Statistica Sinica、Journal of Multivariate Analysis、Computational Statistics and Data Analysis、Journal of Statistical Planning and Inference、Australian & New Zealand Journal of Statistics、etc.
教学情况
本科：概率论与数理统计、多元统计分析
研究生：数理统计与随机过程、统计软件选讲、现代管理统计
招生要求
欢迎有较强的数学、概率统计基础、以及具有一定软件基础和英语基础的学生报考统计学的博士研究生、学术型研究生和应用统计的专业硕士研究生！！！！
Useful Link
☆Journal of the Royal Statistical Society: Series B (Statistical Methodology)
☆Journal of the American Statistical Association Homepage of JASA
☆Bioinformatics
☆Annals of Statistics
☆Biometrics
☆Biometrika
☆Statistica Sinica
☆Scandinavian Journal of Statistics
☆Bernoulli
☆arxiv.org
☆小木虫，科学网，中国数学资源网
☆中国科学、数学学报、应用数学学报、系统科学与数学、应用概率统计、工程数学学报、高校应用数学学报、数学进展
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