Name: Wang Shaoli
Title: Associate Professor
Research Direction: Machine Learning
Courses taught: Statistical Learning, Time Series, Experimental Design, Probability Theory
E - mail: swang@shufe.edu.cn; Phone: 65901906
Research Field
Machine Learning, Graph Models, Causal Inference
Education Experience
2000—2005 Penn State University, USA Doctor of Philosophy in Statistics
Work Experience
2009—Present Shanghai University of Finance and Economics School of Statistics and Data Science Associate Professor
2005—2009 Postdoctoral Fellow, Yale University, USA
Research Achievements
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.
