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Yuexiao Dong

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Yuexiao Dong

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Associate Professor

Biography

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 Annals of Statistics, JASA and Biometrika. His proposal "New Developments in Sufficient Dimension Reduction" has been funded by National Science Foundation.

Research Interests

  • Sufficient dimension reduction
  • High-dimensional inference
  • Machine learning and data mining

Courses Taught

Number

Name

Level

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. Real-time sufficient dimension reduction through principal least-squares support-vector machines. Virtual.

  • 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. Model-free variable selection with matrix-valued predictors.

  • Dong, Y., Yu, Z., & Zhu, L. 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.

  • Dong, Y. A review of sufficient dimension reduction.

  • Dong, Y. Model-free variable selection with matrix-valued predictors.

  • 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.

  • Dong, Y. On dual model-free variable selection with two groups of variable. Grand Rapids, MI.

  • Dong, Y. On dual model-free variable selection with two groups of variables. Gainesville, FL.

  • Dong, Y. On dual model-free variable selection with two groups of variables. Denver, CO.

  • 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.W., 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. 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.

  • Dong, Y. & Li, Z. (2018). On sliced inverse regression with missing values. Journal of Nonparametric Statistics. doi: 10.1080/10485252.2018.1508677.

  • Bharadwaj, N., Noble, C., Tower, A., Smith, L., & Dong, Y. (2017). Predicting Innovation Success in the Motion Picture Industry: The Influence of Multiple Quality Signals. Journal of Product Innovation Management, 34(5), 659-680. doi: 10.1111/jpim.12404.

  • Xia, Q. & Dong, Y. On a new hybrid estimator for the central mean space. Journal of Systems Science and Complexity, 30(1), 111-121. Springer Science and Business Media LLC. doi: 10.1007/s11424-017-6227-0.

  • Dong, Y., Kai, B., & Yu, Z. (2017). Dimension reduction via local rank regression. Journal of Statistical Computation and Simulation, 87(2), 239-249. doi: 10.1080/00949655.2016.1205067.