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Junlong Zhao 发布时间:2016-10-23 浏览次数:

AFFILIATION AND ADDRESS

School of Statistics,

Beijing Normal University,

Beijing, China, 100875.

Emailzhaojunlong928@126.com

EDUCATION

Beijing Institution of Technology,

l  Ph.D,  Statistics,  03/2007.

l  M.S.,  Statistics,  04/2004

PROFESSIONAL EXPERIENCE

Associate Professor, 2013-present

School of Statistics, Beijing Normal Univerity (2016-present)

Department of Mathematics,Beihang University, (07/2013-12/2015)

Assistant Professor  05/2007-07/2013

Department of Mathematics, Beihang University,

Visiting Scholar  03/2014-03/2015

Department of Statitics, University of North Carolina at Chapel Hill.

Research Assisitant  05/2013-07/2013

Department of Mathematics, Hong Kong Baptist University

Research Assistant  03/2011-09/2011   

Department of Statistics.  National University of Singapore.

RESEARCH  INTERESTS

Dimension reduction, High dimensional data analysis, Statistical learning;

PUBLICATIONS

1)        Junlong Zhao, Chenlei Leng (2016) A analysis on high dimensional penalized interaction model. Bernoulli, 22(3), 1937–1961.

2)        Xiaoling Lv, Junlong Zhao, Yu Chen, Hansheng Wang, (2016) A choice model with a diverging choice set for POI data analysis. Statistics and Its Interface, 9, 355–363

3)        Xuhua Liu, Junlong Zhao, Na Li, (2016) A confidence distribution approach to inferring the among-group variance component in one-way random effects model with unequal error variances, Journal of Statistical Planning and Inference,  2016, (171), 79-91.

4)        Lele Huang, Junlong Zhao*, Huiwen Wang, Siyang Wang.(2016)  Robust Shrinkage Estimation and Selection for Functional Multiple Linear Model Through LAD Loss. Computational statistics & data analysis. doi. 10.1016/j.csda. 2016.05.017

5)        Junlong Zhao (2015)  General sparse boosting: improving the feature selection of L2boosting.  Communication in statistics--computation and simulation, 44(6),  1612-1640.

6)        Junlong Zhao, Chenlei, Leng, (2014) Structured lasso for regression with matrix covariates. Statistic Sinica, 24, 799-814.

7)        Junlong Zhao, Chenlei Leng, Lexin Li, and Hansheng Wang (2013) High-dimensional influence measure. The Annals of  Statistics 41(5), 2639-2667.

8)        Yin Yuliang, Zhao JunlongTesting normal means the reconcilability of the p-value and the Bayesian evidenceThe Scientific World Journal, Volume 2013 (2013), Article ID 381539, 7 pages. pp 1-7.  

9)        Junlong Zhao (2013) Asymptotic convergence of dimension reduction based boosting in classification. Journal of Statistical Planning and Inference 143, 651–662

10)     Junlong Zhao, Hongyu Guan (2012) Consistency of Two Stage Method in Classification:  Dimension Reduction Boosting. 2012 International Conference on Systems and Informatics (ICSAI2012) 2238—2241

11)     Junlong Zhao (2012) Sparse boosting with correlation based penalty. Lecture Notes in Artificial Intelligence 7713, 161--172.

12)     Junlong Zhao (2012) Modeling by combining dimension reduction and L2boosting, Lecture Notes in Computer Science,  7389   230--235.

13)     Junlong Zhao, Yuliang Yin, Xingzhong Xu, (2012) Penalized weighted variance estimate for dimension reduction, Communication in statistics--Theory and Method. 41(3),453-473.

14)       Junlong Zhao, Xiuli Zhao.(2010).Dimension reduction using generalized gradient direction. Computational  Statistics and Data Analysis  54, 1089--1102

15)     Junlong Zhao, Xingzhong, Xu. (2009). Dimension reduction based on  weighted     variance estimate. Science in China: Series A Mathematics, 52(3) ,539--560 . 

16)     Jianjun Ma, Xingzhong Xu,  Junlong Zhao. (2008).  Inverse  regression  in  Binary Response LDV Model.  Communications   in Statistics--Theory and  Methods. 37, 233--246.

Research  Project

2012.1—2014.12

Project supported by National Science Foundation of China “Robust dimension reduction and variable selection in high dimensional data analysis”11101022

2015.1-2018.12

“Dimension reduction for high dimensional data of complex structures”.  Project supported by National Science Foundation of China.

 




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