Yuexiao Dong

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 Areas

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

Education

• Ph.D. Statistics , Pennsylvania State University, (2004-2009)
• M.S. Statistics , Pennsylvania State University, (2004-2009)
• Tsinghua University, Major: Mathematics, B.S,Tsinghua University, (2000-2004)

Experience

• July 2016 – present, Associate Professor (with tenure), Department of Statistical Science, Temple University
• July 2009 – June 2016, Assistant Professor, Department of Statistical Science, Temple University

Sample Publications

• Li, B. and Dong, Y. (2009) Dimension Reduction for Non-Elliptically Distributed Predictors. Annals of Statistics, 37, 1272-1298.
• Dong, Y. and Li, B. (2010) Dimension Reduction for Non-Elliptically Distributed Predictors: Second-Order Methods. Biometrika, 97, 279-294.
• Dong, Y. and Yu, Z. (2012) Dimension Reduction for the Conditional kth Moment via Central Solution Space. Journal of Multivariate Analysis, 112, 207-218.
• Zhu, L. P., Dong, Y., and Li, R. (2013) Semi-Parametric Estimation of Conditional Heteroscedasticity via Single-Index Modeling. Statistica Sinica, 23, 1235-1255.
• Bharadwaj, N. and Dong, Y. (2014) Toward Further Understanding the Market-sensing Capability–Value Creation Relationship. Journal of Product Innovation Management, 31, 799-813.
• Yu, Z., Dong, Y. and Shao, J. (2016) On marginal sliced inverse regression for ultrahigh dimensional model-free feature selection. The Annals of Statistics, 44, 2594-2623.
• Yu, Z., Dong, Y. and Zhu, L. X. (2016) Trace pursuit: a general framework for model-free variable selection. Journal of the American Statistical Association, 111, 813-821.
• Bharadwaj, N., Noble, C., Tower, A., Smith, L. and Dong, Y. (2017) Predicting innovation success in the motion picture industry: the influence of multiple quality signals. Journal of Product Innovation Management, 34, 659-680.

Teaching

• Stat 8108: Applied Multivariate Analysis
• Stat 2521: Data Analysis and Statistical Computing
• Stat 8109: Regression and Time Series