Yuexiao Dong

Yuexiao Dong

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Associate Professor

      • Gilliland Research Fellow


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




STAT 2521

Data Analysis and Statistical Computing


STAT 3502

Regression and Predictive Analytics


BA 9814

Advanced Quantitative Research Methods


STAT 8108

Applied Multivariate Analysis I


Selected Publications


  • Dong, Y., Soale, A., & Power, M.D. (2023). A selective review of sufficient dimension reduction for multivariate response regression. Journal of Statistical Planning and Inference, 226, 63-70. Elsevier BV. doi: 10.1016/j.jspi.2023.02.003.

  • 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


  • 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.

  • Dong, Y. & Li, Z. (2018). On sliced inverse regression with missing values. Journal of Nonparametric Statistics, 30(4), 990-1002. Informa UK Limited. doi: 10.1080/10485252.2018.1508677.

  • Dong, Y., Xia, Q., Tang, C., & Li, Z. (2018). On sufficient dimension reduction with missing responses through estimating equations. Computational Statistics and Data Analysis, 126, 67-77. doi: 10.1016/j.csda.2018.04.006.

  • Dong, Y., Alothman, A., & Artemiou, A. (2018). On dual model-free variable selection with two groups of variables. Journal of Multivariate Analysis, 167, 366-377. Elsevier Inc..

  • Dong, Y. & Zhang, Y. (2018). On a new class of sufficient dimension reduction estimators. Statistics and Probability Letters, 139, 90-94. doi: 10.1016/j.spl.2018.03.019.

  • Babb, P., Zhang, L., Allin, P., Wallgren, A., Wallgren, B., Blunt, G., Garrett, A., Murtagh, F., Smith, P.W., Elliott, D., Nason, G., Powell, B., Moore, J.C., Durrant, G.B., Smith, P.A., Chambers, R.L., Herzberg, A.M., Pilling, M., Appleby, W., Barnett, A., Bhansali, R., Bharadwaj, N., Dong, Y., Brakel, J.v.d., Budd, L., Doidge, J., Gilbert, R., Francis, B., Frisoli, K., Nugent, R., Perez, F.J.G., Lara, L., Porcu, E., Henry, S., Hunt, I., Ieva, F., Gasperoni, F., Jansson, I., Kumar, K., Longford, N., Manninen, A., Mateu, J., McNicholas, P.D., McNicholas, S.M., Tait, P.A., Mehew, J., Oberski, D.L., Ruiz, M., Yohai, V.J., Zamar, R., Stehlik, M., Stehlikova, S., Soza, L.N., Towers, J., & Wijayatunga, P. (2018). Statistical challenges of administrative and transaction data. JOURNAL of the ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS in SOCIETY, 181(3), 578-605. Retrieved from http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000434143700002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=abcd71df5a6dac31fd219478b0a9c638.