Biography
Yuexiao Dong is an Associate Professor from the Department of Statistical Science. Dr. Dong received his Bachelor’s degree in mathematics from Tsinghua University. He obtained his PhD from
the statistics department of the Pennsylvania State University in 2009.
Dr. Dong’s research focuses on sufficient dimension reduction and high-dimensional data analysis. His research articles have been published in top-tier journals such as The Annals of Statistics, Journal of the American Association, and Biometrika. His proposal “New Developments in Sufficient Dimension Reduction” has been funded by the National Science Foundation. Dr. Dong has served as an Associate Editor for the Journal of Systems Science and Complexity since 2015.
Research Interests
- Sufficient dimension reduction
- High-dimensional inference
- Machine learning and data mining
Courses Taught
Number | Name | Level |
|---|---|---|
STAT 2501 | Quantitative Foundations for Data Science | Undergraduate |
STAT 2521 | Data Analysis and Statistical Computing | Undergraduate |
STAT 3502 | Regression and Predictive Analytics | Undergraduate |
BA 9814 | Advanced Quantitative Research Methods | Graduate |
STAT 8108 | Applied Multivariate Analysis I | Graduate |
Selected Publications
Recent
Soale, A. & Dong, Y. (2022). On sufficient dimension reduction via principal asymmetric least squares. JOURNAL of NONPARAMETRIC STATISTICS, 34(1), 77-94. 10.1080/10485252.2021.2025237
Zhou, T., Dong, Y., & Zhu, L. (2021). TESTING THE LINEAR MEAN AND CONSTANT VARIANCE CONDITIONS IN SUFFICIENT DIMENSION REDUCTION. STATISTICA SINICA, 31(4), 2179-2194. 10.5705/ss.202019.0095
Soale, A. & Dong, Y. (2021). On expectile-assisted inverse regression estimation for sufficient dimension reduction. Journal of Statistical Planning and Inference, 213, 80-92. doi: 10.1016/j.jspi.2020.11.004.
Power, M. & Dong, Y. (2021). Bayesian model averaging sliced inverse regression. Statistics and Probability Letters, 174. doi: 10.1016/j.spl.2021.109103.
Artemiou, A., Dong, Y., & Shin, S. (2021). Real-time sufficient dimension reduction through principal least squares support vector machines. Pattern Recognition, 112. doi: 10.1016/j.patcog.2020.107768.
Dong, Y. (2021). A brief review of linear sufficient dimension reduction through optimization. Journal of Statistical Planning and Inference, 211, 154-161. doi: 10.1016/j.jspi.2020.06.006.
Dong, Y. (2021). Sufficient Dimension Reduction Through Independence and Conditional Mean Independence Measures. In Festschrift in Honor of R. Dennis Cook (pp. 167-180). doi: 10.1007/978-3-030-69009-0_8.
Li, Z. & Dong, Y. (2021). Model-Free Variable Selection With Matrix-Valued Predictors. Journal of Computational and Graphical Statistics, 30(1), 171-181. doi: 10.1080/10618600.2020.1806854.
Dong, Y., Yu, Z., & Zhu, L. (2020). Model-free variable selection for conditional mean in regression. Computational Statistics & Data Analysis, 152, 107042-107042. Elsevier BV. doi: 10.1016/j.csda.2020.107042.
Shen, C., Chen, L., Dong, Y., & Priebe, C. (2020). Sparse Representation Classification beyond ℓ1 Minimization and the Subspace Assumption. IEEE Transactions on Information Theory, 66(8), 5061-5071. doi: 10.1109/TIT.2020.2981309.
Tang, C., Fang, E., & Dong, Y. (2020). High-dimensional interactions detection with sparse principal hessian matrix. Journal of Machine Learning Research, 21.
Power, M. & Dong, Y. (2020). Comment on ‘Review of sparse sufficient dimension reduction’. Statistical Theory and Related Fields. doi: 10.1080/24754269.2020.1829394.