General Info

Dr. Wu received his Ph.D. in Statistics from The University of North Carolina at Chapel Hill in May 2006. He took a postdoctoral position at Princeton University from August 2006 to July 2008 and joined North Carolina State University in August 2008. His current research interests include: Longitudinal Data Analysis, Machine Learning, Model Selection, Nonparametetric and Semiparametric Statistics.

Teaching

• ST 790 - Multivariate Statistics (Fall 2016)
  
  Office hours: Tuesdays 2pm--3pm, Thursdays 11am-noon, or by appointment in 4274 SAS Hall.
  TA office hour: Mondays 9am-10:30am and Wednesdays 12:30pm-2pm in 1101 SAS Hall.


• ST 370 - Probability and Statistics for Engineers:
      Fall 2008, Spring 2009, Fall 2009, Spring 2010


• ST 372 - Introduction to Statistical Inference and Regression:
      Fall 2010, Fall 2014


• ST 422 - Introduction to Mathematical Statistics II:
      Spring 2016


• ST 521 - Statistical Theory I:
      Fall 2011


• ST 790 - Nonparametric Regression and Smoothing:
      Spring 2012, Fall 2013

Editorial Boards

• Associate Editor for Journal of the American Statistical Association (A&CS), 2016--present.
• Associate Editor for Journal of Computational and Graphical Statistics, 2013--present.
• Associate Editor for Bernoulli Journal, 2014--2015.
• Guest Assoociate Editor for Statistica Sinica, 2014.

Selected Publications

• Shin, S. J., Wu, Y., Zhang, H. H., and Liu, Y. (2016+). Principal Weighted Support Vector Machines for Sufficient Dimension Reduction in Binary Classification. Biometrika, In press.
• White, K., Stefanski, L. A., and Wu, Y. (2016+). Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods. Journal of the American Statistical Association, In press.
• Chang, J., Tang, C. Y., and Wu, Y. (2016). Local Independence Feature Screening for Nonparametric and Semiparametric Models by Marginal Empirical Likelihood. Annals of Statistics, 44, 515-539.
• Zhang, X., Wu, Y., Wang, L. and Li, R. (2016). Variable Selection for Support Vector Machines in Moderately High Dimensions. Journal of the Royal Statistical Society, Series B, 78, 53-76.
• Wu, Y. and Stefanski, L. A. (2015). Automatic structure recovery for additive models. Biometrika, 102, 381-395.
• Yao, F., Lei, E. and Wu, Y.. (2015). Effective dimension reduction for sparse functional data. Biometrika, 102, 421-437.
• Ke, T., Fan, J. and Wu, Y. (2015). Homogeneity Pursuit. Journal of the American Statistical Association, 110, 175-194.
• Stefanski, L. A., Wu, Y., and White, K. (2014). Variable Selection in Nonparametric Classification via Measurement Error Model Selection Likelihoods. Journal of the American Statistical Association, 109, 574-589.
• Zhou, H. and Wu, Y. (2014). A Generic Path Algorithm for Regularized Statistical Estimation. Journal of the American Statistical Association, 109, 686-699.
• Chang, J., Tang, C. Y., and Wu, Y. (2013). Marginal Empirical Likelihood and Sure Independence Feature Screening. Annals of Statistics, 41, 2123-2148.
• Müller, H.-G., Wu, Y., and Yao, F. (2013). Continuously Additive Models for Nonlinear Functional Regression. Biometrika, 100, 607-622.

Click here for a complete list of publications.

Software