Yuexiao Dong

Profile Picture of Yuexiao Dong

Yuexiao Dong

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Associate Professor

      • Gilliland Research Fellow

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

  • Dong, Y. & Li, Z. (2024). A note on marginal coordinate test in sufficient dimension reduction. Statistics & Probability Letters, 204, 109947-109947. Elsevier BV. doi: 10.1016/j.spl.2023.109947.

  • Kai, B., Huang, M., Yao, W., & Dong, Y. (2023). Nonparametric and Semiparametric Quantile Regression via a New MM Algorithm. Journal of Computational and Graphical Statistics, 32(4), 1613-1623. Informa UK Limited. doi: 10.1080/10618600.2023.2184374.

  • 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.

  • Power, M. & Dong, Y. (2020). Comment on ‘Review of sparse sufficient dimension reduction’. Statistical Theory and Related Fields. doi: 10.1080/24754269.2020.1829394.

  • Tang, C., Fang, E., & Dong, Y. (2020). High-dimensional interactions detection with sparse principal hessian matrix. Journal of Machine Learning Research, 21.