Wang Shanshan PhD (Statistics)

AssociateProfessor (Quantitative Economics and Business Statistics)

Master's Supervisor

Email: sswang@buaa.edu.cn

Office: A931

• Research Areas:

High-dimensional data analysis; Non-parametric statistical analysis; Machine Learning; Survival data analysis; Statistical algorithm and applications

• Education Background:

PhD Statistics Beijing Normal University 2011/09-2014/07

Master Statistics Beijing Normal University 2008/09-2011/07

Bachelor Applied Mathematics Qingdao University 2004/09-2008/07

• Oversea Background:

Post-doc Statistics Nanyang Technology University Singapore

2014/08-2015/12

Research AssociateStatistics Nanyang Technology University Singapore

2013/09-2014/05

• Work Experience:

Associate Prof. Quantitative Economics and Business Statistics BUAA-SEM

2015/12-2019/08

Assistant Prof. Quantitative Economics and Business Statistics BUAA-SEM

2015/12-2019/08

• Courses:

• Master & PhD:

  Advanced Economics; Statistics with applications in R; Statistics; Nonparametric and Semi-parametric Models

• Undergraduate

  Applied Statistics; Time Series Analysis; Non-parametricstatisticsanalysis

• Project

1. High-dimensional Hypothesis testing based on Penalized Likelihood and Empirical Likelihood methods, National Natural Science Foundation of China (Grant No. 11701023), 2018.01-2020.12. In charge

2. Ultra-high-dimensional complex data modeling and statistical inference theory, methods and applications, (Grant No.KG16044701), in charge

3. Research on discriminant analysis of oil compositional data, 2018-2019. In charge

4. Research on Pesticide Residue Index and its Construction, Scientific Research Project supported by Enterprise (Grant No.KH54034101), 2016—2017. In charge

5. Research on Visualization of Pesticide Residue Data, Scientific Research Project supported by Enterprise (Grant No.KH54034301) 2016—2017. In charge

• Selected Papers:

  (See details inhttps://www.researchgate.net/profile/Shanshan_Wang24)

1. TingtingHuang, Saporta Gilbert,HuiwenWang,ShanshanWang*. A Robust Spatial Autoregressive Scalar-on-Function Rregression with t-distribution.Advances in Data Analysis and Classification, 2020, Published online:

2. Wang Huiwen, Liu Ruiping,Wang Shanshan*. Ultra-high dimensional variable screening via Gram–Schmidt orthogonalization.Computational Statistics,2020,35, 1153-1170

3. Xiaokang Wang, Huiwen Wang,Shanshan Wang*, Jidong Yuan.Convex Clustering Method for Compositional Data via Sparse Group Lasso, Neurocomputing,2020,accepted (21 Oct 2020)

4. Zhichao Wang, Huiwen Wang,Shanshan Wang*, Shan Lu, Gilbert Saporta.Linear mixed-effects model for longitudinal complex data with diversified characteristics,Journal of Management Science and Engineering, 2020, 5(2), 105-124

5. Zhichao Wang, Huiwen Wan

   ,Shanshan Wang*. Linear Mixed-Effects Model for Multivariate Longitudinal Compositional Data.Neurocomputing,2019,335: 48-58.

6. Huiwen Wang, Zhichao Wang &Shanshan Wang* . Sliced inverse regression method for multivariate compositional data modeling,Statistical Papers,2019,Published online:

7. Wang Huiwen, Gu Jie,Wang Shanshan*,Gilbert Saporta. Spatial partial least squares autoregression: Algorithm and applications,Chemometrics and Intelligent Laboratory Systems,2019,184: 123-131

8. Wang Shanshan, Xiang Liming*. Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates.Statistics and Computing, 2017, 27(5): 1347-1364.

9. Wang Shanshan, Xiang Liming*. Two-layer EM-algorithm for ALD mixture regression models: A new solution to composite quantile regression.Computational Statistics & Data Analysis, 2017, 115:136-154.

10. Wang Shanshan, Cui Hengjian*. Partial penalized empirical likelihood ratio test under sparse case.Acta Mathematica Applicatae Sinica (English Series), 2017, 32(2): 327-344

11. Wang Zhichao, Wang Huiwen,Wang Shanshan*, Lu Shan, Gilbert Saporta. Linear mixed-effects model for longitudinal complex data with diversified characteristics.Journal of Management Science and Engineering,2019,Online:

12. Jie Gu, Lihong Wang, Huiwen Wang,Shanshan Wang*. A novel approach to intrusion detection using SVM ensemble with feature augmentation.Computers & Security, 2019, 86:53-62

13. Yu Yang, Zou Zhihong,Wang Shanshan*. Statistical regression modeling for energy consumption in wastewater treatment.Journal of Environmental Sciences, 2019, 75: 201-208.

14. Haitao Zheng, Jie Hu,Shanshan Wang*, Huiwen Wang. Examining the influencing factors of CO2 emissions at city level via panel quantile regression: evidence from 102 Chinese cities.Applied Economics, 2019, 51(35): 3906-3919.

15. Wang Huiwen, Huang Tingting,Wang Shanshan*. A Flexible Spatial Autoregressive Modelling Framework for Mixed Covariates of Multiple Data Types,Communications in Statistics-Simulation and Computation, 2019, DOI:10.1080/03610918.2019.1626885

16. Yuan Wei, Huiwen Wang,Shanshan Wang*, Saporta, Gilbert. Incremental modelling for compositional data streams.Communications in Statistics- Simulation and Computation,2019, 48(8):2229-2243

17. Wang Shanshan, Hu Tao*, Cui Hengjian. Adjusted empirical likelihood inference for additive hazards regression.Communication in Statistics-Theory and Methods, 2016, 45(24):7294-7305

18. Wang Shanshan, Cui Hengjian. Empirical Likelihood Inference for Partially Linear Errors in Variables models with covariate data missing at random.Acta Mathematica Applicatae Sinica (English series), 2016, 32(2), 305-318.

19. Wang Shanshan, Cui Hengjian*, Li, Runze. Empirical Likelihood Inference for Semi-parametric Estimating Equations.Science China Mathematics, 2013, 56: 1247–1262.

20. Wang Shanshan,Cui Hengjian*. Partial Penalized Likelihood Ratio Test under Sparse Case.Statistics,2013.

21. Liu Ruiping, Wang Huiwen,Wang Shanshan*. Functional variable selection via Gram- Schmidt orthogonalization for multiple functional linear regression.Journal of Statistical Computation and Simulation, 2018, 88(18): 3664-3680

22. Yang Yu, Zhihong Zou,Shanshan Wang*. Bayesian quantile regression and variable selection for partial linear single-index model: Using free knot spline,Communications in Statistics - Simulation and Computation,2018,48(5),1429-1449

23. Wang Huiwen, Gu Jie,Wang Shanshan*. An effective intrusion detection framework based on SVM with feature augmentation.Knowledge-Based Systems, 2017,136:130-139.

24. Wei Yuan,Wang Shanshan*, Wang Huiwen. Interval-valued data regression using partial linear model.Journal of Statistical Computation and Simulation, 2017, 87(16): 3175-3194

25. Zhou Jiantao,Wang Shanshan*, Zhou Jianbo, Xu Yanli. Measurement of the severity of opportuneistic fraud in personal injury insurance: evidence from China.Emerging Markets Finance and Trade, 2017, 53(2): 387-399

26. Wang Shanshan, Zhao Tianhao, Zheng Haitao*, Hu Jie. The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model.Sustainability, 2017, 9(12), 2237

27. Yu Yang, Zou Zhihong,Wang Shanshan, Renate Meyer*. Bayesian non-parametric modelling of the link function in the single-index model using a Bernstein-Dirichlet process prior.Journal of Statistical Computation and Simulation, 2019, 89(17): 3290-3312

28. Peng Mengjiao, Xiang Liming*,Wang Shanshan. Semiparametric regression analysis of clustered survival data with semi-competing risks,Computational Statistics & Data Analysis, 2018, 124:53-70

29. Zheng Haitao, Hu Jie, Guan Rong* andWang Shanshan. Examining Determinants of CO2 Emissions in 73 Cities in China.Sustainability,2016,8(12), 1296.

30. Wang H, Zhang Y, Lu S andWang S*. Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19.F1000Research,2020, 9:333

31. Lu, S.,Wang, S*., Wang, H. PLSGLR-based Approach for Risk Analysis on Peer-to-peer Internet Finance Platforms in China. The 9th International Conference on PLS and Related Methods. 2017 June Macau.

32. Zhou Jiantao, Ai Jing,Wang Shanshanand Wang Tianyang.Economic and Non-economic Losses Claim Effects on the Severity of Opportunistic Fraud in Auto Bodily Injury Compulsory Liability (BICL) Insurance: Evidence from China.7th China International Conference on Insurance and Risk Management (CICIRM),2016,,358-397

• Journal Referee

  Knowledge-Based Systems;

  Computational Statistics & Data Analysis;

  Statistical Papers

  Journal of Statistical Computation and Simulation;

  Communications in Statistics-Simulation and Computation

• Selected Presentations

  The 2019 IMS-China International Conference on Statistics and Probability, Dalin, China

  The 9^thInternational Conference on PLS and Related Methods, Macau, China 2017

  International Conference on Energy Finance, Hangzhou, 2017

  The 10^thICSA International Conference, Shanghai, 2016

  International Workshop on Advances in Data Science, Beijing, 2016

  The 2^thInternational Symposium on Interval Data Modelling: Theory and Applications, Xiamen, 2016

  The1^stInternational Conference onBig Data & Applied Statistics,Beijing, 2014

  The16^thand 17^th5.4 Youth Meeting of Probability and Statistics in Beijing-Tianjin Area, Beijing, 2012 and 2013

  The 9^thAnnual Probability and Statistics, Tianjin, 2010

• Selected Awards

  Zhong Jiaqing Outstanding Paper Award,2012

  Excellent Academic Scholarship, 2004-2008,2010,2013,2014

  Excellent Graduate Student, 2012

  The Second Race Scholarship, 2010

  Excellent Student Cadres, 2010

  The 6^thNational Post-Graduate Mathematic Contest in Modeling, Third Prize, 2010

  National Scholarship (Twice), 2005, 2006