Email: qinss@tjufe.edu.cn qinsslzu@gmail.com
研究兴趣：
High-dimensional Data & Big Data, Clustering & Regression Clustering, Machine learning, Artificial intelligence, M-estimation, Bayesian estimation, Change Point, Interdisciplinary application
研究工作经历：
2021.01-Now School of Statistics, Tianjin University of Finance and Economics
2020.07-2020.12 Postdoctoral fellowship, York University, Canada.
教育经历：
2015.09–2020.04 Ph.D., Statistics, York University, Canada.
2014.09–2015.08 Ph.D., Physical Geography, Lanzhou University, China.
2011.09–2014.06 Master of Science, Lanzhou University, China.
2007.09–2011.06 Bachelor of Science, Henan University of Technology, China.
Paper Submitted to Journal or In progress
1. Dimension reduction via Bayesian Grouped-Gibbs Sampling. In progress.
2. Grapgh-based change point detection for tensor flow. In progress.
3. Qin, S., Sun, B., Wu, Y. and Fu,Y*. Generalized Least-Squares Fitting in Dimension Expansion Method for Nonstationary Processes. Submitted to Environmetrics. Revision 1 submitted.
Publications (Note: * 通讯作者)
1. 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.
2. Qin, S., Ding, H.* and Wu, Y. (2020) Non-negative feature selection and/or grouping. Annals of the Institute of Statistical Mathematics. https://doi.org/10.1007/s10463-020-00766-z.
3. Qin, S.* and Wu, Y. (2020) General Matching Quantiles M-estimation. Computational Statistics & Data Analysis, 106941.
4. 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.
5. Wu, Y.*, Sun, X., Chan, E., and Qin, S. (2017). Detecting non-negligible new influences in environmental data via a general spatio-temporal autoregressive Model. International Journal of Environment and Climate Change, 223-235.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. Jiang, P., Qin, S.*, Wu, J., and Sun, B. (2015). Time series analysis and forecasting for wind speeds using support vector regression coupled with artificial intelligent algorithms. Mathematical Problems in Engineering, 2015.
13. 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.
14. 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.
15. Wang, J., Jiang, H.*, Zhou, Q., Wu, J., and Qin, S. (2016). China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model. Renewable and Sustainable Energy Reviews, 53, 1149-1167.
16. Wu, J., Wang, J.*, Qin, S., and Lu, H. (2016). Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm. International journal of green energy, 13(11), 1145-1162.
