Bibliography

Prof. WANG Huiwen received her Bachelor’s degree in Applied Mathematics from Beihang University in 1982, her Master’s degree in Decision Mathematics (DEA MASE) from University Paris Dauphine (Paris IX) in 1989, and her Doctor’s degree in Management Engineering from Beihang University in 1992.

Prof. WANG Huiwen is the professor in School of Economics and Management (SEM), Beihang University. She is now the Deputy Director of the Academic Committee of Beihang University, Director of the Academic Committee of SEM, Dean of Zhizhen College, and Directors of Beijing Experimental Teaching Demonstration Center of Innovative Economy and Intelligent Management, Complex Data Analysis Research Center of Beihang University, as well as the Sino-French Data Science Laboratory of Beihang University. She has been visiting professors/scholars at the Group HEC, CNAM,INRIA of France, and University of Hong Kong.  

Teaching courses

Applied Statistics (undergraduate student)

Quantitative Analysis and Management Decision (EMBA)

Management Statistics (MBA)

Research interest

Statistical methods of complex data analysis with applications in economic management

Academic experience

Mainly engaged in research of the statistical methods of complex data analysis with applications in economic management. Hosted more than 20 research projects, including the National 863 Program, Major Projects, Major International Cooperation Project, General Project and Entrusted Project by the National Natural Science Foundation of China (NSFC). She has also hosted the Doctoral Program Foundation by the Ministry of Education, Beijing Natural Science Foundation. In addition, she has carried out applied research programs for many government departments and enterprises. Has published 5 academic monographs and over 150 papers.

Main papers and monographs

1.H. Wang, J. Meng, Automatic modeling methods for variable selection and model classification[M].Beijing Science Press. 2013 (in Chinese).

2.V. Esposito Vinzi, W.W. Chin, J. Henseler, H. Wang, Handbook of Partial Least Square: Concepts, Methods and Application[M]. Springer, 2009.

3.H. Wang, Z. Wu, J. Meng, Linear and non-linear methods for partial least square[M]. National Defense Industry Press, 2006 (in Chinese).

4.H. Wang, Method and application on partial least square regression[M]. National Defense Industry Press, 1999 (in Chinese).

5.R. Ren, H. Wang, Multivariate statistical data analysis: Theory, method and application[M]. National Defense Industry Press, 1997 (in Chinese).

6.Z. Wang, H. Wang, S. Wang. Linear mixed-effects model for multivariate longitudinal compositional data[J]. Neurocomputing, 2019, 335: 48-58.

7.H. Wang, Z. Wang, S. Wan. Sliced inverse regression method for multivariate compositional data modeling[J]. Statistical Papers, 2019: 1-33.

8.H. Wang, S. Lu, J. Zhao. Aggregating multiple types of complex data in stock market prediction: A model-independent framework[J]. Knowledge-Based Systems, 2019, 164: 193-204.

9.J. Gu, L. Wang, H. Wang, et al. A novel approach to intrusion detection using SVM ensemble with feature augmentation[J]. Computers & Security, 2019, 86:53-62.

10.H. Wang, T. Huang, S. Wang. A Flexible Spatial Autoregressive Modelling Framework for Mixed Covariates of Multiple Data[J]. Communications in Statistics - Simulation and Computation, 2019, doi:10.1080/03610918.2019.1626885.

11.H. Wang, J. Gu, S. Wang, G. Saporta. Spatial Partial Least Squares Autoregression: Algorithm and Applications[J]. Chemometrics and Intelligent Laboratory Systems, 2019, 184: 123-131.

12.R. Liu, H. Wang, S. Wang, Functional variable selection via Gram–Schmidt Orthogonalization for multiple functional linear regression[J]. Journal of Statistical Computation and Simulation, DOI: 10.1080/00949655.2018.1530776

13.H. Wang, Y. Wang, R. Ren, B. Xia, S. Wang, Forecasting method research on cash flow statement in kind[J]. Journal of Management Science and Engineering, 2018, 21(9): 1-11 (in Chinese).

14.S. Lu, J. Zhao, H. Wang, R. Ren. Herding boosts too-connected-to-fail risk in stock market of China[J]. Physica A: Statistical Mechanics and its Applications. 2018,505, pp.945-964.

15.Y. Wei, J. Gu, H. Wang, et al. Uncovering the culprits of air pollution: Evidence from China's economic sectors and regional heterogeneities[J]. Journal of Cleaner Production, 2018, 171: 1481-1493.

16.Y. Wei, H. Wang, S. Wang, G. Saporta. Incremental modeling for compositional data streams[J]. Communications in Statistics - Simulation and Computation. DOI: 10.1080/ 03610918.2018.1455870.

17.H. Wang, J. Gu, S. Wang. An effective intrusion detection framework based on SVM with feature augmentation[J]. Knowledge-Based Systems, 2017, 136: 130-139.

18.Y. Wei, S. Wang, H. Wang. Interval-valued data regression using partial linear model[J]. Journal of Statistical Computation and Simulation, 2017, 87(16): 3175-3194.

19.B. Xia, H. Wang, R. Zhou, What Contributes to Success in MOBA Games? An Empirical Study of Defense of the Ancients 2[J]. Games and Culture, 2017, DOI: 1555412017710599.

20.H. Wang, M. Chen, X. Shi, N. Li, Principal Component Analysis for Normal-Distribution-Valued Symbolic Data[J]. IEEE Transactions on Cybernetics. 2016, 46(2): 356-365.

21.H. Wang, C. Wang, H. Zheng, H. Feng, R. Guan, W. Long, Updating Input-Output Tables with Benchmark Table Series[J]. Economic Systems Research, 2015, 27(3): 287-305.

22.H. Wang, L. Shangguan, R. Guan, Principal Component Analysis for Compositional Data Vectors[J]. Computational Statistics. 2015, 30(4): 1079-1096.

23.L. Huang, H. Wang, H. Cui, S. Wang. Sieve M-estimator for a Semi-functional Linear Model[J]. Science China-Mathematics, 2015, 58(11): 2421-2434.

24.M. Chen, H. Wang, Z. Qin. Principal Component Analysis for Probabilistic Symbolic Data: a More Generic and Accurate Algorithm[J]. Advances in Data Analysis and Classification, 2015, 9(1): 59-79.

25.L. Huang, H. Wang, A. Zheng. The M-estimator for Functional Linear Regression Model[J]. Statistics & Probability Letters, 2014, 88: 165-173.

26.H. Wang, L. Shangguan, J. Wu, R. Guan. Multiple linear Regression Modeling for Compositional Data[J]. Neurocomputing, 2013,122, 490-500.

27.H. Wang, B. Xia, J. Meng, Fast Gram-Schmidt regression method[J].Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(09): 1259-1262 (in Chinese).

28.H. Wang, R. Guan, J. Wu. CIPCA: Complete-information-based Principal Component Analysis for Interval-valued Data[J]. Neurocomputing, 2012, 86：158-169.

29.H. Wang, R. Guan, J. Wu, Linear Regression of Interval-valued Data based on Complete Information in Hypercubes[J]. Journal of Systems Science and Systems Engineering. 2012, 21(4): 422-442.

30.H. Wang, B. Yi, M. Ye,Variable screening based on primary basis analysis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(11): 1288-1291 (in Chinese).

31.W. Long, M.K. Mok, Y. Hu, H. Wang. The Style and Innate Structure of the Stock Markets in China[J]. Pacific-Basin Finance Journal, 2009, 17(2): 224-242 .

32.H. Wang, Q. Liu, M．K. Mok, L. Fu, W.M. Tse, A Hyperspherical Transformation Forecasting Model for Compositional Data[J], European Journal of Operational Research, 2007, 179, 459-468.

33.H. Wang, Q. Liu, Y. Tu, Interpretation of Partial Least-Squares Regression Models with VARIMAX Rotation[J]. Computational Statistics & Data Analysis 2005, 48(1): 207-219.

34.H. Wang, L. Fu, Y. Lechevallier, Disaster Pattern of Flood and Waterlog in Poyang Lake[J], Italian Journal of Applied Statistics, 2001, 13(2): 141-157.

35.H. Wang, Q. Liu, Forecast Modeling for Rotations of Principal Axes of Multi-Dimensional Data Set[J], Computational Statistics & Data Analysis, 1998, 27(3): 345-354.