Seminar 2021-09-24

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

Yuexiao Dong
Associate Professor of Statistical Science, Fox School of Business, Temple University

Date: Friday, September 24, 2021
Time: 11am–noon
Location: Virtual
Zoom info: This virtual talk will be live-streamed via Zoom. For connection information, please contact statistics@gmu.edu.

Abstract

We propose a real-time approach for sufficient dimension reduction. Compared with popular sufficient dimension reduction methods including sliced inverse regression and principal support vector machines, the proposed principal least squares support vector machines approach enjoys better estimation of the central subspace. Furthermore, this new proposal can be used in the presence of streamed data for quick real-time updates. It is demonstrated through simulations and real data applications that our proposal performs better and faster than existing algorithms in the literature.

About the speaker

Yuexiao Dong is Associate Professor and Gilliland Research Fellow from the Department of Statistical Science, Fox School of Business, Temple University. Dr. Dong obtained his Ph.D. from the Pennsylvania State University in 2009. Dr. Dong’s primary research focus is on sufficient dimension reduction and high-dimensional data analysis. His research has been published in statistical journals such as The Annals of Statistics, Journal of the American Statistical Association, and Biometrika. His proposal “New Developments in Sufficient Dimension Reduction” has been funded by the National Science Foundation.

Dr. Dong's other research interests include machine learning and business analytics. His collaborative work has been published in Journal of Machine Learning Research, IEEE Transactions on Information Theory, Pattern Recognition, and Journal of Product Innovation Management. Dr. Dong has served as an Associate Editor for the Journal of Systems Science and Complexity since 2015.