Selected Publications
 White, K., Stefanski, L. A., and Wu, Y. (2017+). Variable Selection in Kernel Regression Using Measurement Error Selection Likelihoods. Journal of the American
Statistical Association, In press.
 Shin, S. J., Wu, Y., Zhang, H. H., and Liu, Y. (2017). Principal Weighted Support Vector Machines for Sufficient
Dimension Reduction in Binary Classification. Biometrika, 104, 6781.
 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, 515539.
 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, 5376.
 Wu, Y. and Stefanski, L. A. (2015). Automatic structure recovery for additive models. Biometrika, 102, 381395.
 Yao, F., Lei, E. and Wu, Y.. (2015). Effective dimension reduction for sparse functional data. Biometrika, 102, 421437.
 Ke, T., Fan, J. and Wu, Y. (2015). Homogeneity Pursuit. Journal of the American
Statistical Association, 110, 175194.
 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, 574589.
 Zhou, H. and Wu, Y. (2014). A Generic Path Algorithm for Regularized Statistical Estimation. Journal of the American
Statistical Association, 109, 686699.
 Chang, J., Tang, C. Y., and Wu, Y. (2013). Marginal Empirical Likelihood and Sure Independence Feature Screening.
Annals of Statistics, 41, 21232148.
 Müller, H.G., Wu, Y., and Yao, F. (2013). Continuously Additive Models for Nonlinear Functional
Regression.
Biometrika, 100, 607622.
Click here for a complete list of publications.

