姓 名：王绍立
职 称：副教授
研究方向：机器学习
教授课程：统计学习、时间序列、实验设计、概率论
E - mail：swang@shufe.edu.cn；电话：65901906
研究项目
序号 | 项目名称 | 项目编号 | 项目来源 | 起止时间 | 项目经费 |
研究领域
机器学习、图模型、因果推断
教育经历
2000—2005 美国宾夕法尼亚州立大学 统计学博士
工作经历
2009—现在 上海财经大学统计与数据科学学院 副教授
2005—2009 美国耶鲁大学博士后
研究成果
1.Zhang, Y., Chen, S., and Wang, S. (2024). Monotone estiation for the coefficient functions of varying coefficient models. Statistics and Its Interface, Accepted.
2. Liu, X., Zhang, Z., Guo, S., and Wang, S. (2024). A Novelty Update and Propagation-based Dynamic Graph Neural Networks and Its Application to Consumer Finance Data. Statistics and Its Interface, 17, 439-450.
3. Liu, X, Zheng, Q., Shen, X., and Wang, S. (2022). An iterative algorithm to learn from positive and unlabeled examples. Statistica Sinica, 32, 961-982.
4. Ma, Y., Wang, S., Xu, L., and Yao, W. (2021). Semiparametric mixture regression with unspecified error distributions. TEST, 30, 429-444.
5. Zhang, Y., Shen, X., and Wang, S. (2020). Large multiple graphical model inference via bootstrap. Statistica Sinica, 30, 695-717.
6.Huang, M., Yao, W., Chen, Y. (2018). Statistical inference and applications of mixture of varying coefficient models. Scandinavian Journal of Statistics.45 618-643.
7.Zhang, Y., and Wang, S. (2018). Monotone function estimation in partially linear Models.Statistics and Its Interface, 11, 19-29.
8.Huang, M., Wang, S., Wang, H., and Jin, T. (2018). Maximum smoothed likelihood estimation for a class of semiparametric Pareto mixture densities. Statistics and Its Interface, 11, 31-40.
9.Huang, G., Wang, S., Wang, X., You, N. (2016). An empirical Bayes method for genotyping and SNP detection using multi-sample next-generation sequencing data. Bioinformatics, 32, 32403-245.
10.Wang, S., Huang, M., and Wu, X., and Yao, W. (2016). Mixture of functional linear models and its application to CO2-GDP functional data. Computational Statistics and Data Analysis, 97, 115.
11.Wen, C, Wang, X., and Wang, S. (2015). Laplace error penalty based variable selection in high dimension. Scandinavian Journal of Statistics, 42, 685-700.
12.Wang, S., Yao, W., and Huang, M. (2014). A note on the identifiability of nonparametric and semiparametric mixtures of GLMs. Statistics and Probability Letters, 93, 41-45.
13.Huang, M., Li, R., and Wang, S. (2013). Nonparametric mixture of regression models. Journal of the American Statistical Association, 108, 929-941.
