Publications

• Weighted distance weighted discrimination and its asymptotic properties. Qiao, X., Zhang, H. H. Liu, Y., Todd, M. and Marron, S. (2009) Journal of American Statistical Association, to appear.
  
  
• The adaptive COSSO for nonparametric surface estimation and model selection. Storlie, C., Bondell, H., Reich, B. and Zhang, H. H. (2009) Statistica Sinica, to appear.
  
  
• Variable Selection for Semiparametric Mixed Models in Longitudinal Studies Xiao, N., Zhang, D., and Zhang H. H. (2009) Biometrics, to appear.
  
  
• Automatic Model Selection for Partially Linear Models Xiao, N., Zhang, H. H., and Zhang, D. (2009) Journal of Multivariate Analysis, 100, 2100-2111.
  
  
• FIRST: Combining forward iterative selection and shrinkage in high dimensional sparse linear regression. Hwang, W. Y., Zhang, H. H., and Ghosal, S. (2009) Statistics and Its Interface,2, 341-348.
  
  
• On The Adaptive Elastic-Net With A Diverging Number of Parameters. Zou, H. and Zhang, H. H. (2009). Annals of Statistics, 37, 1733-1751.
  
  
• Discussion of Sure independence screening for ultra-high dimensional feature space by Fan and Lv. . Zhang, H. H. (2008) Journal of the Royal Statistical Society, Series B, 70.
  
  
• Support Vector Machine Classification for High Dimensional Microarray Data Analysis, with Applications in Cancer Research. Zhang, H. H. (2008) High-Dimensional Data Analysis in Cancer Research, Li and Xu, eds. Springer-Verlag.
  
  
• A new chi-square approximation to the distribution of non-negative definite quadratic form in non-central normal variables. Liu, H., Tang, Y. and Zhang, H. H. (2008) Computational Statistics and Data Analysis, 53, 853-856.
  
  
• Variable selection for multicategory SVM via sup-norm regularization. Zhang, H. H., Liu, Y., Wu, Y. and Zhu,J. (2008). Electronic Journal of Statistics, 2, 149-167.
  
  
• Adaptive-LASSO for Cox's Proportional Hazard Model. Zhang, H. H. and Lu, W. (2007). Biometrika, 94, 691-703. Advance access through the link.
  
  
• Variable selection for proportional odds model. Lu, W. and Zhang, H. H. (2007). Statistics in Medicine , 26, 3771-3781.
  
  
• Support Vector Machines with adaptive Lq penalties. Liu, Y., Zhang, H. H., Park, C. and Ahn, J (2007). Computational Statistics and Data Analysis , 51(12), 6380-6394.
  
  
• Nonparametric Model Selection in Hazard Regression. Leng, C. and Zhang, H. H. (2007) Institute of Statistics Mimeo Series 2576, NC State University. Journal of Nonparametric Statistics , 18(7), 417 - 429.
  
  
• Component Selection and Smoothing for Nonparametric Regression in Exponential Families. Zhang, H. H. and Lin, Y (2006) Institute of Statistics Mim eo Series 2554, NCSU. Statistica Sinica, 16, 1021-1042.
  
  
• Gene Selection Using Support Vector Machines With Nonconvex Penalty. Zhang, H. H., Ahn, J., Lin, X., and Park, C. (2006) Institute of Statistics Mimeo Series 2574, NCSU. Bioinformatics, 22 (1), 88-95. Advance access published on October 25, 2005. You may download the MATLAB code here.
  
  
• Variable Selection for Support Vector Machines via Smoothing Spline ANOVA. (pdf) Zhang, H. H. (2006) Instistute of Statistics Mimeo Series 2572, NCSU. Statistica Sinica, 16(2), 659-674.
  
  
• Component Selection and Smoothing in Smoothing Spline Analysis of Variance Models -- COSSO (ps). Lin, Y. and Zhang, H. H. (2006) Institute of Statistics M imeo Series 2556, NCSU. Revised version (pdf) Annals of Statistics 34 (5), 2272-2297. We present a new regulazation approach to automate variable selection in nonparametric smoothing regression. The MATLAB and R codes available here.
  
  
• Multiclass Proximal Support Vector Machines. Tang, Y. and Zhang, H. H. (2005) Instistute of Statistics Mimeo Series 2573, NCSU. Journal of Computational and Graphical Statistis, 15 (2), 339-355.
  
  
• Variable Selection and Model Building via Likelihood Basis Pursuit (pdf). Zhang, H. H., Wahba, G., Lin, Y., Voelker, M., Ferris, M., Klein, R. and Klein, B. (2004) Institute of Statistics Mimeo Series 2558, NCSU. Journal of American Statistical Association, 99, 659-672.
  
  
• Model Building with Likelihood Basis Pursuit. Ferris M., Voelker, M, and Zhang, H. H. (2004) Institute of Statistics Mimeo Series 2 555, NCSU. Journal of Optimization Methods and Software, 19(5), 577-594.
  
  
• Statistical Properties and Adaptive Tuning of Support Vector Machines. Lin, Y., Wahba, G., Zhang, H. H., and Lee, Y. (2002) Machine Learning , 48, 115-136.
  
  
• Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines. Wahba, G., Lin, Y., Lee, Y. and Zhang, H. H. (2002) Nonlinear Estimation and Classification, Denison, Hansen, Holmes, Mallick and Yu, eds, Springer, 125-143.
  
  
• Generalized Approximate Cross Validation for Support Vector Machines, or, Another Way to Look at Margin-like Quantities. Wahba, G., Lin, Y. and Zhang, H. H. (2000) Advances in Large Margin Classifiers, Smola, Bartlett, Scholkopf and Schurmans, eds., MIT Press, 297-309.

Proceedings and Recent Technical Reports

• Penalized Asymptotic Likelihood Approach for Linear Transformation Model Selection. Zhang, H. H., Lu, W and Wang, H. (2007) Institute of Statistics Mimeo Series 2604, NCSU.
  
  
• Variable Selection for Multicategory SVM via Sup-norm Regularization. Zhang, H. H., Liu, Y., Wu, Y. and Zhu, J. (2006) Institute of Statistics Mimeo Series 2596, NCSU.
  
  
• Compactly Supported Radial Basis Function Kernels. Zhang, H. H. and Genton, G. M. and Liu, P. (2004) Institute of Statistics Mimeo Series 2570, NCSU. submitted.
  
  
• Variable Selection via Penalized Likelihood with Adaptive Penalty. Lu, W. and Zhang, H. H. (2006) Institute of Statistics Mimeo Series 2594, NCSU.
  
  
• Nonparametric Variable Selection and Model Building via Likelihood Basis Pursuit. Zhang, H. H. (2002) Ph.D. Thesis, University of Wisconsin at Madison.
  
  
• Variable Selection via Basis Pursuit for Non-Gaussian Data. Zhang, H. H., Wahba, G., Lin, Y., Voelker, M., Ferris, M., Klein, R. and Klein, B. (2001) Proceedings of the Joint Statistical Meetings.
  
  
• On the Relation between the GACV and Joachims' \xi\alpha method for tuning Support Vector Machines, with extension to the nonstandard case. Wahba, G., Lin, Y., Lee, Y. and Zhang, H. H. (2001) Technical Report 1039, Department of Statistics, University of Wisconsin at Madison.

Software

• Nonparametric model selection: COSSO page
• Variable selection for SVM: SCAD-SVM
• Censored data analysis: Adaptive Cox

Research Activities

• RTP Statistics Working Group (2009-)
• Data Mining Working Group (2005-2006)
• SAMSI DMML (2003-2004)
• SAMSI model selection group (2002-2003)