 Yichao Wu, PhD
Investigator Associate Professor North Carolina State University NCSU Statistics Department 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, Variable Selection, Dimension Reduction, and Nonparametetric Statistics. He is a member of the American Statistical Association, the Institute of Mathematical Statistics, and the International Chinese Statistical Association and also serves as an associate editor for the Journal of Computational and Graphical Statistics and the Bernoulli Journal. |
| Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks., Xue H, Wu S, Wu Y, Idarraga JCRamirez, Wu H, Stat Med, Volume 37, 2018 Jul 30, Issue 17, p.2630-2644. (2018) PubMed |
| Variable selection in kernel regression using measurement error selection likelihoods., White KR, Stefanski LA, Wu Y, J Am Stat Assoc, Volume 112, 2017, Issue 520, p.1587-1597. (2017) PubMed |
| Variable selection for support vector machines in moderately high dimensions., Zhang X, Wu Y, Wang L, Li R, J R Stat Soc Series B Stat Methodol, Volume 78, 2016 Jan, Issue 1, p.53-76. (2016) PubMed |
| A consistent information criterion for support vector machines in diverging model spaces., Zhang X, Wu Y, Wang L, Li R, J Mach Learn Res, Volume 17, 2016, Issue 16, p.1-26. (2016) PubMed |
| Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood., Chang J, Tang CYong, Wu Y, Ann Stat, Volume 44, 2016, Issue 2, p.515-539. (2016) PubMed |
| On quantile regression in reproducing kernel Hilbert spaces with data sparsity constraint., Zhang C, Liu Y, Wu Y, J Mach Learn Res, Volume 17, 2016 Apr, Issue 40, p.1-45. (2016) PubMed |
| Automatic structure recovery for additive models., Wu Y, Stefanski LA, Biometrika, Volume 102, 2015 Jun 02, Issue 2, p.381-395. (2015) PubMed |
Domain selection for the varying coefficient model via local polynomial regression., Kong D, Bondell H, Wu Y, Comput Stat Data Anal, Volume 83, 2015 Mar 01, p.236-250. (2015)  R code for analysis of Boston Housing data PubMed |
| Probability-enhanced sufficient dimension reduction for binary classification., Shin SJun, Wu Y, Zhang HHelen, Liu Y, Biometrics, Volume 70, 2014 Sep, Issue 3, p.546-55. (2014) PubMed |
| Variable selection in nonparametric classification via measurement error model selection likelihoods., Stefanski LA, Wu Y, White K, J Am Stat Assoc, Volume 109, 2014, Issue 506, p.574-589. (2014) PubMed |
| Two-dimensional solution surface for weighted support vector machines., Shin SJun, Wu Y, Zhang HHelen, J Comput Graph Stat, Volume 23, 2014 Apr 03, Issue 2, p.383-402. (2014) PubMed |
| Adaptively weighted large margin classifiers., Wu Y, Liu Y, J Comput Graph Stat, Volume 22, 2013, Issue 2, (2013) PubMed |
| Hard or soft classification? Large-margin unified machines., Liu Y, Zhang HHelen, Wu Y, J Am Stat Assoc, Volume 106, 2011 Mar 01, Issue 493, p.166-177. (2011) PubMed |
| Utility-based weighted multicategory robust support vector machines., Liu Y, Wu Y, He Q, Stat Interface, Volume 3, 2010 Oct 01, Issue 4, p.465-476. (2010) PubMed |
| skda: Sparse (multicategory) kernel discriminant analysis (R)., Stefanski LA, Wu Y, White K, (2013) |