薛原
薛原,男,汉族,民盟,博士,副教授,博士生导师
金融风险管理师
教育经历
2002-2006 中央民族大学 本科
2006-2008 美国密西西比州立大学 硕士
2009-2012 美国佐治亚大学 博士
工作经历
2013-2015 对外经济贸易大学 讲师
2016-至今 对外经济贸易大学 副教授、硕士生/博士生导师
2025-至今 中国石油大学(北京)克拉玛依校区 援疆援建
讲授课程
1. 《高等数学》
研究方向
1. 高维数据分析与计算
2. 商业分析
科研项目
1. 国家社科基金重大项目子课题负责人,在研。
2. 人力资源和社会保障部项目负责人,在研。
3. 随机矩阵/数组形式高维数据的充分降维:统计理论、方法及其应用,国家自然科学基金委,已结项。
4. 基于逆矩估计量的高维空间降维方法,校级课题,已结项。
5. 大数据中高维数据分析创新理论与实践,对外经济贸易大学惠园优秀青年学者项目,已结项。
代表性论文
1. Robust selection and estimation for sparse multivariate functional nonparametric additive models via regularized Huber regression. (2025). Journal of Computational and Applied Mathematics.
2. Estimation in functional additive models for high-dimensional functional data. (2025). SCIENCE CHINA mathematics.
3. A structured covariance ensemble for sufficient dimension reduction. (2023). Advances in Data Analysis and Classification.
4. Model averaging estimation for generalized partially linear varying coefficient models. (2023). STAT.
5. An ensemble of inverse moment estimators for sufficient dimension reduction. (2021). Computational Statistics and Data Analysis.
6. Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness. (2019). Biometrics.
7. Simultaneous estimation for semiparametric multiindex models. (2019). Journal of Statistical Computation and Simulation.
8. A general SDR approach via Hellinger integral. (2018). Journal of Statistical Planning and Inference.
9. An R package for model fitting, model selection, and the simulation for longitudinal data with dropout missingness. (2018). Communication in Statistics – Simulation and Computation.
10. Sufficient dimension reduction using Hilbert-Schmidt independence criterion. (2017). Computational Statistics and Data Analysis.
11. Ensemble sufficient dimension folding methods on analyzing matrix-valued data. (2016). Computational Statistics and Data Analysis.
12. Sufficient dimension folding for a functional of conditional distribution of matrix- or array-valued objects. (2015). Journal of Nonparametric Statistics.
13. Sufficient dimension folding for regression mean function. (2014). Journal of Computational and Graphical Statistics.
联系方式
E-mail:2025563008@cupk.edu.cn