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个人简介

王惠文,1974年毕业于江西省南昌县莲塘一中,1974年至1976年在莲塘一中任教师,1977年至1978年为江西省造纸厂化验室工人。1982年于北京航空航天大学应用数学专业获学士学位,1989年于法国巴黎多芬大学(Paris XI)决策数学系获硕士学位(DEA MASE),1992年于北航获管理工程专业工学博士学位。 现任北京航空航天大学经济管理学院教授(博士生导师),并任校学术委员会副主任,院学术委员会主任,致真书院院长,“创新经济和智慧管理北京市实验教学示范中心”主任,北航复杂数据分析研究中心主任,北航中法数据科学实验室主任;曾任第十、第十一、第十二届北京市政协常委,第十一、第十二届北京市政协提案委员会副主任;任第八、第九届民建中央委员,第八、第九届民建北京市委副主委。现为国际统计学会会员、国际统计计算学会会员、中国统计教育学会常务理事、全国统计教材编审委员会委员、中国大数据专家委员会委员,国家自然科学基金委员会学科评审组成员。曾在法国高等商业学院、法国国立工艺学院、法国国家自动化信息研究所、香港大学任客座教授和访问学者。是国家杰出青年科学基金项目获得者,享受国务院政府特殊津贴专家。

研究领域

经济管理中复杂数据统计分析的理论、方法与应用研究

近期论文

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王惠文、孟洁,变量筛选、模型分类及自动化建模方法,北京:科学出版社,2013. V.Esposito Vinzi,W.W.Chin,J.Henseler,H.Wang,Handbook of PartialLeast Square:Concepts,Methods andApplication. Springer, 2009. 王惠文、吴载斌、孟杰,偏最小二乘回归的线性与非线性方法,北京:国防工业出版社,2006. 王惠文,偏最小二乘回归方法与应用,北京:国防工业出版社,1999. 任若恩、王惠文,多元统计数据分析—理论、方法、实例,北京:国防工业出版社,1997. Z. Wang, H. Wang, S. Wang. Linear mixed-effects model for multivariate longitudinal compositional data[J]. Neurocomputing, 2019, 335: 48-58. H. Wang, Z. Wang, S. Wan. Sliced inverse regression method for multivariate compositional data modeling[J]. Statistical Papers, 2019: 1-33. 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. 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. 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. 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. R.Liu, H. Wang, S. Wang. Functional variable selection via Gram–SchmidtOrthogonalization for multiple functional linear regression, Journal ofStatistical Computation and Simulation, DOI: 10.1080/00949655.2018.1530776 王惠文,王玉茹,任若恩,夏棒,王珊珊.实物资金流量表的预测方法研究,管理科学学报, 2018, 21(9):1~11. S.Lu, J. Zhao, H. Wang, R Ren. Herding boosts too-connected-to-fail risk in stockmarket of China. Physica A: Statistical Mechanics and its Applications. 2018,505, pp.945-964. Wei Y, Gu J, Wang H, 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. Y.Wei, H. Wang, S. Wang, G. Sporta. Incremental modeling for compositionaldata streams. Communications in Statistics - Simulation and Computation.DOI: 10.1080/ 03610918.2018.1455870. H. Wang, J. Gu, S.Wang. An effective intrusion detection framework based on SVM with featureaugmentation. Knowledge-Based Systems, 2017, 136: 130-139. Y. Wei, S. Wang, H.Wang. Interval-valued data regression using partial linear model[J]. Journal ofStatistical Computation and Simulation, 2017, 87(16): 3175-3194. B. Xia, H. Wang, R.Zhou. What Contributes to Success in MOBA Games? An Empirical Study ofDefenseof the Ancients 2. Games and Culture, 2017, DOI: 1555412017710599. H.Wang, M.Chen, X.Shi,and N.Li. Principal Component Analysis for Normal-Distribution-Valued SymbolicData, IEEE Transactions on Cybernetics. 2016, 46(2): 356-365. H. Wang, C. Wang,H.Zheng, H.Feng, R.Guan, W.Long. Updating Input-Output Tables with BenchmarkTable Series,Economic Systems Research, 2015,27(3):287-305. H.Wang, L.Shangguan,R.Guan. Principal Component Analysis for Compositional Data Vectors.Computational Statistics. 2015, 30(4):1079-1096. L. Huang, H. Wang, H.Cui, S. Wang. Sieve M-estimator for a Semi-functional Linear Model. ScienceChina-Mathematics, 2015,58(11):2421-2434. M. Chen, H. Wang, Z.Qin. Principal Component Analysis for Probabilistic Symbolic Data: a MoreGeneric and Accurate Algorithm. Advances in Data Analysis and Classification.2014. 2015, 9(1):59-79. L. Huang, H. Wang, A.Zheng. The M-estimator for Functional Linear Regression Model. Statistics &Probability Letters, 2014, 88: 165-173. H. Wang, L. Shangguan,J. Wu, R. Guan. Multiple linear Regression Modeling for Compositional Data.Neurocomputing, 2013,:122, 490-500. 王惠文,夏棒,,孟洁,快速Gram-Schmidt回归方法,北京航空航天大学学报, 2013, 39(09):1259-1262. H. Wang, R. Guan, J. Wu.CIPCA: Complete-information-based Principal Component Analysis forInterval-valued Data. Neurocomputing, 2012, 86:158-169. H. Wang, R. Guan, J. Wu,Linear Regression of Interval-valued Data based on Complete Information inHypercubes. Journal of Systems Science and Systems Engineering. 2012, 21(4) :422-442. 王惠文,仪彬,叶明.基于主基底分析的变量筛选.北京航空航天大学学报,2008,34(11):1288-1291. W. Long,M.K.Mok,Y. Hu,H. Wang.The Style and InnateStructure of the Stock Markets in China,Pacific-Basin Finance Journal.2009, 17(2):224-242 . H. Wang, Q. Liu, M.K. Mok, L. Fu, W.M. Tse,A Hyperspherical Transformation Forecasting Model for Compositional Data,European Journal of Operational Research, 2007, 179, 459-468. Wang, Q. Liu, Y. Tu:Interpretation of Partial Least-Squares Regression Models with VARIMAX Rotation.Computational Statistics & Data Analysis 2005,48(1): 207-219. Wang,L. Fu, Y.Lechevallier, Disaster Pattern of Flood and Waterlog in Poyang Lake, ItalianJournal of Applied Statistics, 2001, 13(2):P141-157. H.Wang, Q.Liu,Forecast Modelingfor Rotations of Principal Axes of Multi-Dimensional DataSet, Computational Statistics & Data Analysis, 1998, 27(3):345-354.

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