Publications of Yichao Wu


Statistical Methodology Papers

  • Feng, Y., Wu, Y., and Stefanski, L. A. (2017+). Nonparametric Independence Screening via Favored Smoothing Bandwidth, Journal of Statistical Planning and Inference, In press.
  • 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.
  • Kong, D., Bondell, H. D. and Wu, Y. (2017+). Fully efficient robust estimation, outlier detection, and variable selection via penalized regression. Statistica Sinica, In press.
  • Zou, J., Wang, F. and Wu, Y. (2017+). Large Portfolio Allocation Using High-Frequency Financial Data. Statistics and Its Interface, In press.
  • Zhang, X., Wang, C. and Wu, Y. (2017+). Functional envelope for model-free sufficient dimension reduction. Journal of Multivariate Analysis, 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, 67-81.
  • Shin, S. J., Zhang, H. H. and Wu, Y. (2017). A Nonparametric Survival Function Estimator via Censored Kernel Quantile Regressions. Statistica Sinica, 27, 457-478.
  • Hu, H., Yao, W. and Wu, Y. (2017). The Robust EM-type Algorithms for Log-concave Mixtures of Regression Models. Computational Statistics and Data Analysis, 111, 14-26.
  • Peng, B., Wang, L., and Wu, Y. (2016). An Error Bound for L1-norm Support Vector Machine Coefficients. Journal of Machine Learning Research, 17(236), 1-26.
  • Zhang, C., Liu, Y. and Wu, Y. (2016). On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint. Journal of Machine Learning Research, 17(40), 1-45.
  • 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.
  • Hu, H., Wu, Y. and Yao, W. (2016). Maximum Likelihood estimation of mixture of log-concave densities. Computational Statistics and Data Analysis, 101, 137-147.
  • Zhang, X., Wu, Y., Wang, L. and Li, R. (2016). A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces. Journal of Machine Learning Research, 17(16), 1-26.
  • Yao, F., Wu, Y. and Zou, J. (2016). Probability enhanced effective dimension reduction for classifying sparse functional data. Test (invited paper), 25, 1-22.
  • Yao, F., Wu, Y. and Zou, J. (2016). Rejoinder on: Probability enhanced effective dimension reduction for classifying sparse functional data. Test, 25, 52-58.
  • Sun, W., Liu, Y., Crowley, J. J., Chen, T.-H., Zhou, H., Chu, H., Huang, S., Kuan, P.-F., Li, Y., Miller, D., Shaw, G., Wu, Y., Zhabotynsky, V., McMillan, L., Zou, F., Sullivan, P. F. and de Villena, F. P. M. (2015). IsoDOT detects differential RNA-isoform expression/usage with respect to a categorical or continuous covariate with high sensitivity and specificity. Journal of American Statistical Association (A&CS), 110, 975-986.
  • Xiao, W., Wu, Y. and Zhou, H. (2015). ConvexLAR: An Extension of Least Angle Regression. Journal of Computational and Graphical Statistics, 23, 603-626.
  • 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.
  • Davenport, C. A., Maity, A. and Wu, Y. (2015). Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood. Journal of Nonparametric Statistics, 27, 195-213.
  • Kong, D., Bondell, H. D. and Wu, Y. (2015). Domain selection for the varying coefficient model via local polynomial regression. Computational Statistics and Data Analysis, 83, 236-250.
  • Shin, S. J., Wu, Y., Zhang, H. H. and Liu, Y. (2014). Probability-enhanced sufficient dimension reduction for binary classification. Biometrics, 70, 546-555.
  • Wu, S., Xue, H., Wu, Y. and Wu, H. (2014). Variable Selection for Sparse High-Dimensional Nonlinear Regression Models by Combining Nonnegative Garrote and Sure Independence Screening. Statistica Sinica, 24, 1365-1387.
  • Avery, M., Wu, Y., Zhang, H. H. and Zhang, J. (2014). RKHS-based functional nonparametric regression for sparse and irregular longitudinal data. The Canadian Journal of Statistics, 42, 204-216.
  • 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.
  • Shin, S. J., Wu, Y. and Zhang, H. H. (2014). Two-Dimensional Solution Surface for Weighted Support Vector Machines. Journal of Computational and Graphical Statistics, 23, 383-402.
  • Zeng, P. and Wu, Y. (2013). Coordinate great circle descent algorithm with application to single-index models. Statistics and Its Interface. 6, 511-518.
  • 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.
  • Fan, J., Maity, A., Wang, Y. and Wu, Y. (2013). Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data. Journal of Nonparametric Statistics, 25, 109-128.
  • Wu, Y. and Liu, Y. (2013). Adaptively Weighted Large Margin Classifiers. Journal of Computational and Graphical Statistics, 22, 416-432.
  • Wu, Y. and Liu, Y. (2013). Functional robust support vector machines for sparse and irregular longitudinal data. Journal of Computational and Graphical Statistics, 22, 379-395.
  • Wu, T., and Wu, Y. (2012). Nonlinear Vertex Discriminant Analysis with Reproducing Kernels. Statistical Analysis and Data Mining, 5, 167-176.
  • Wang, L., Wu, Y. and Li, R. (2012). Quantile Regression for Analyzing Heterogeneity in Ultra-high Dimension. Journal of the American Statistical Association, 107, 214-222.
  • Wu, Y. (2012). Elastic net for Cox�s proportional hazards model with a solution path algorithm. Statistica Sinica, 22, 271-294.
  • Liu, Y., Zhang, H. H. and Wu, Y. (2011). Soft or hard classification? Large margin unified machines. Journal of the American Statistical Association, 106, 166-177.
  • Liu, Y. and Wu, Y. (2011). Simultaneous multiple non-crossing quantile regression estimation using kernel constraints. Journal of Nonparametric Statistics, 23, 415-437.
  • Wu, Y. and Li, L. (2011). Asymptotic Properties of Sufficient Dimension Reduction with A Diverging Number of Predictors. Statistica Sinica, 21, 707-730.
  • Wu, Y. (2011). An ordinary differential equation-based solution path algorithm. Journal of Nonparametric Statistics, 23, 185-199.
  • Wu, Y. and Liu, Y. (2011). Non-crossing large-margin probability estimation and its application to robust SVM via preconditioning. Statistical Methodology, 8, 56-67.
  • Liu, Y., Wu, Y. and He, Q. (2010). Utility-based weighted multicategory robust support vector machines. Statistics and Its Interface, 3, 465-476.
  • Wu, Y., Fan, J. and Müller, H.-G. (2010). Varying-coefficient functional linear regression. Bernoulli, 16, 730-758.
  • Wu, Y., Zhang, H. H. and Liu, Y. (2010). Robust Model-free Multiclass Probability Estimation. Journal of the American Statistical Association, 105, 424-436.
  • Zhu, Z. and Wu, Y. (2010). Estimation and prediction of a class of convolution-based spatial nonstationary models for large spatial data. Journal of Computational and Graphical Statistics, 19, 74-95.
  • Fan, J., Richard S. and Wu, Y. (2009). Ultrahigh dimensional feature selection: beyond the linear model. Journal of Machine Learning Research, 10, 1829-1853.
  • Wu, Y. and Liu, Y. (2009). Stepwise Multiple Quantile Regression Estimation using Non-crossing Constraints. Statistics and Its Interface, 2, 299-310.
  • Fan, J., Wu, Y., and Feng, Y. (2009). Local quasi-likelihood with a parametric guide. Annals of Statistics, 37, 4153-4183.
  • Fan, J., Feng, Y., and Wu, Y. (2009). Network exploration via the adaptive LASSO and SCAD penalties. Annals of Applied Statistics, 3, 2, 521-541.
  • Wu, Y. and Liu, Y. (2009). Variable selection in quantile regression. Statistica Sinica, 19, 801-817.
  • Fan, J. and Wu, Y. (2008). Semiparametric estimation of covariance matrices for longitudinal data. Journal of the American Statistical Association, 103, 484, 1520-1533.
  • Zhang, H. H., Liu, Y., Wu, Y. and Zhu, J. (2008). Variable selection for the multicategory SVM via sup-norm regularization. Electronic Journal of Statistics, 2, 149-167.
  • Wu, Y. and Liu, Y. (2007). Robust truncated-hinge-loss support vector machines. Journal of the American Statistical Association, 102, 479, 974-983.
  • Liu, Y. and Wu, Y. (2007). Variable selection via a combination of the L0 and L1 penalties. Journal of Computational and Graphical Statistics, 16, 4, 782-798.
  • Liu, Y. and Wu, Y. (2006). Optimizing psi-learning via mixed integer programming. Statistica Sinica, 16, 2, 441-457.

Discussion

  • Shin, S. J. and Wu, Y. (2014). Discussion of "Variable Selection in Large Margin Classifier-based Probability Estimation with High-Dimensional Predictors" by Kruppa et al., Biometrical Journal, 56, 594-596.

Collaborative Work

  • MAQC Consortium (2010). The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology, 28, 8, 827-841.
  • Ribeiro, C., Hurd, H., Wu, Y., Braun, M., Jones, L., Brighton, B., Boucher, R. and O'Neal, W. (2009). Azithromycin treatment alters gene expression in inflammatory, lipid metabolism, and cell cycle pathways in well-differentiated human airway epithelia. PLoS ONE, 4(6).

Conference proceedings

  • Wu, Y. and Liu, Y. (2007). On Multicategory Truncated-Hinge-Loss Support Vector Machines. Contemporary Mathematics, 443, 49-58.

Book chapters

  • Fan, J., Fan, Y. and Wu, Y. (2010). High dimensional classification. High-dimensional Data Analysis (T. T. Cai and X. Shen, eds), 3-37, World Scientific, New Jersey.
  • Liu, Y. and Wu, Y. (2010). Flexible Large Margin Classifiers. High-dimensional Data Analysis (T. T. Cai and X. Shen, eds), 39-71, World Scientific, New Jersey.
  • Fan, J., Feng, Y. and Wu, Y. (2010). Ultrahigh dimensional variable selection for Cox's proportional hazards model, IMS Collection, 6, 70-86.

This site was updated at August 28, 2017