研究兴趣
高维统计、机器学习、贝叶斯统计、变点等。
主讲课程
统计机器学习、数据挖掘与机器学习实践、AI+机器学习等。
工作经历
2021.01 – 至今 讲师，统计学院，天津财经大学（TUFE）。
2020.07 – 2020.12 博士后，数学与统计系，加拿大约克大学。
教育经历
2015.09 – 2020.04 统计学博士，数学与统计系，加拿大约克大学。
2011.09 – 2014.06 理学硕士，概率论与数理统计，兰州大学。
2007.09 – 2011.06 理学学士，数学与应用数学，河南工业大学。
科研项目
o 国家自然科学青年基金项目，项目名称：“高维异质回归模型的稳健估计及应用”， 2023 – 2025年，主持在研；
o 国家自然科学天元数学基金项目，项目名称：“高维非稀疏贝叶斯估计及其应用”，合作导师：复旦大学朱仲义教授， 2023年，主持结项；
o 国家社会科学基金项目，项目名称：“复杂大数据的子抽样方法及应用研究“，2023-2026年，参与在研；
o 天津财经大学高层次人才项目启动基金，2021年
发表论文
1. Qin, S., Guo, B., Wu, Y., Xie, H., & Dong, J. (2025). A constrained robust Markov regime-switching model for long-term risk evaluation. Journal of Applied Statistics, 1-16.
2. Qin, S., Tan, Z., Wei, D. and Wu, Y. (2025) PCA-uCP: an ensemble method for multiple change-point detection in moderately high-dimensional data. Statistics and computing, 35, 31.
3. Qin, S., Wang, S., Zang, J., and Yuan, P.* (2025) Enhancing CNNs with Detail Feature Module for High-pixel Image Classification. Stat, 14(1) e70034.
4. Qin, S., Zhang, G., Wu, Y., Zhu, Z.* (2024) Bayesian Grouping-Gibbs Sampling Estimation of High-dimensional Linear Model with Non-sparsity. Computational Statistics & Data Analysis, 203, 108072.
5. Qin, S., Tan, Z.*, Wu, Y. (2024) On Robust Estimation of Hidden semi-Markov Regime-switching Models. Annals of Operations Research. 338,1049–1081.
6. Qin, S.*, Zhou, G., Wu, Y. (2023) Change-point detection for Multi-way tensor based framework. Entropy. 25(4), 552.
7. Guo, W., Balakrishnan, N., & Qin, S.* (2023). A modified partial envelope tensor response regression. Stat, 12(1), e615.
8. Jiang, H., Qin,S.*, & Padilla, O.H.M. (2022). Feature Grouping and Sparse Principal Component Analysis with Truncated Regularization. Stat, e538.
9. Shi, X., Qin, S.*, Wu, Y*. (2021) Robust Detection of Abnormality in Highly Corrupted Medical Images. Electronic Journal of Statistics. 15(2): 5283-5309 (2021).
10. Qin, S., Sun, B., Wu, Y. and Fu, Y*. (2021) Generalized Least-Squares Fitting in Dimension Expansion Method for Nonstationary Processes. Environmetrics, 32(7), e2684.
11. Ding, H., Qin, S.*, Wu, Y. and Wu Y. (2021) Asymptotic properties on high-dimensional multivariate regression M-estimation. Journal of Multivariate Analysis. Vol 183, 104730.
12. Qin, S., Ding, H., Wu, Y., & Liu, F. (2021). High-dimensional sign-constrained feature selection and grouping. Annals of the Institute of Statistical Mathematics, 73, 787-819.
13. Qin, S.* and Wu, Y. (2020) General Matching Quantiles M-estimation. Computational Statistics & Data Analysis, 106941.
14. Guo, W.*, Qin, S. and Zhao, Z. (2020) Generalized Ridge and Principal Correlation Estimator of the Regression Coefficient in Growth Curve Model. Linear Algebra and its Applications, 115-113.
15. Qu, J., Qin, S.*, Liu, L., Zeng, J., and Bian, Y. (2016). A hybrid study of multiple contributors to per capita household CO2 emissions (HCEs) in China. Environmental Science and Pollution Research, 23(7), 6430-6442.
16. Qin, S., Wang, J.*, Wu, J., and Zhao, G. (2016). A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China. International journal of green energy, 13(6), 595-607.
17. Qin, S., Liu, F.*, Wang, C., Song, Y., and Qu, J. (2015). Spatial-temporal analysis and projection of extreme particulate matter (PM10 and PM2.5) levels using association rules: A case study of the Jing-Jin-Ji region, China. Atmospheric Environment, 120, 339-350.
18. Song, Y., Qin, S.*, Qu, J., and Liu, F. (2015). The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region. Atmospheric Environment, 118, 58-69.
19. Wang, J., Qin, S.*, Jin, S., and Wu, J. (2015). Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources. Renewable and Sustainable Energy Reviews, 42, 26-42.
20. Wang, J., Qin, S.*, Zhou, Q., and Jiang, H. (2015). Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China. Renewable Energy, 76, 91- 101.
21. Qin, S., Liu, F.*, Wang, J., and Song, Y. (2015). Interval forecasts of a novelty hybrid model for wind speeds. Energy Reports, 1, 8-16.
22. Qin, S., Liu, F.*, Wang, J., and Sun, B. (2014). Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmospheric Environment, 98, 665-675.
