Hao Helen Zhang

Wang, X., Zhang, H. H., & Wu, Y. (2019). Multiclass Probability Estimation With Support Vector Machines. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 28(3), 586–595. https://doi.org/10.1080/10618600.2019.1585260

Caner, M., & Zhang, H. H. (2014). Adaptive Elastic Net for Generalized Methods of Moments. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 32(1), 30–47. https://doi.org/10.1080/07350015.2013.836104

Shin, S. J., Wu, Y. C., & Zhang, H. H. (2014). Two-dimensional solution surface for weighted support vector machines. Journal of Computational and Graphical Statistics, 23(2), 383–402. https://doi.org/10.1080/10618600.2012.761139

Sharma, D. B., Bondell, H. D., & Zhang, H. H. (2013). Consistent Group Identification and Variable Selection in Regression With Correlated Predictors. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 22(2), 319–340. https://doi.org/10.1080/15533174.2012.707849

Lu, W., Zhang, H. H., & Zeng, D. (2013). Variable selection for optimal treatment decision. STATISTICAL METHODS IN MEDICAL RESEARCH, 22(5), 493–504. https://doi.org/10.1177/0962280211428383

Yuan, S., Zhang, H. H., & Davidian, M. (2012). Variable selection for covariate-adjusted semiparametric inference in randomized clinical trials. STATISTICS IN MEDICINE, 31(29), 3789–3804. https://doi.org/10.1002/sim.5433

Liu, Y., Zhang, H. H., & Wu, Y. (2011). Hard or Soft Classification? Large-Margin Unified Machines. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 106(493), 166–177. https://doi.org/10.1198/jasa.2011.tm10319

Zhang, H. H., Cheng, G., & Liu, Y. (2011). Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 106(495), 1099–1112. https://doi.org/10.1198/jasa.2011.tm10281

Storlie, C. B., Bondell, H. D., Reich, B. J., & Zhang, H. H. (2011). Surface estimation, variable selection, and the nonparametric oracle property. Statistica Sinica, 21(2), 679–705.

Rahardja, D., Zhao, Y. D., & Zhang, H. H. (2010). Bayesian Inference of Odds Ratios in Misclassified Binary Data with a Validation Substudy. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 39(10), 1845–1854. https://doi.org/10.1080/03610918.2010.518271

Lu, W., & Zhang, H. H. (2010). On Estimation of Partially Linear Transformation Models. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 105(490), 683–691. https://doi.org/10.1198/jasa.2010.tm09302

Zhang, H. H., Lu, W., & Wang, H. (2010). On sparse estimation for semiparametric linear transformation models. JOURNAL OF MULTIVARIATE ANALYSIS, 101(7), 1594–1606. https://doi.org/10.1016/j.jmva.2010.01.015

Wu, Y., Zhang, H. H., & Liu, Y. (2010). Robust Model-Free Multiclass Probability Estimation. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 105(489), 424–436. https://doi.org/10.1198/jasa.2010.tm09107

Shows, J. H., Lu, W., & Zhang, H. H. (2010). Sparse estimation and inference for censored median regression. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 140(7), 1903–1917. https://doi.org/10.1016/j.jspi.2010.01.043

Ni, X., Zhang, D., & Zhang, H. H. (2010). Variable Selection for Semiparametric Mixed Models in Longitudinal Studies. BIOMETRICS, 66(1), 79–88. https://doi.org/10.1111/j.1541-0420.2009.01240.x

Qiao, X., Zhang, H. H., Liu, Y., Todd, M. J., & Marron, J. S. (2010). Weighted Distance Weighted Discrimination and Its Asymptotic Properties. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 105(489), 401–414. https://doi.org/10.1198/jasa.2010.tm08487

Liu, H., Tang, Y., & Zhang, H. H. (2009). A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 53(4), 853–856. https://doi.org/10.1016/j.csda.2008.11.025

Ni, X., Zhang, H. H., & Zhang, D. (2009). Automatic model selection for partially linear models. JOURNAL OF MULTIVARIATE ANALYSIS, 100(9), 2100–2111. https://doi.org/10.1016/j.jmva.2009.06.009

Hwang, W. Y., Zhang, H. H., & Ghosal, S. (2009). FIRST: Combining forward iterative selection and shrinkage in high dimensional sparse linear regression. Statistics and Its Interface, 2(3), 341–348. https://doi.org/10.4310/sii.2009.v2.n3.a7

Zou, H., & Zhang, H. H. (2009). ON THE ADAPTIVE ELASTIC-NET WITH A DIVERGING NUMBER OF PARAMETERS. ANNALS OF STATISTICS, 37(4), 1733–1751. https://doi.org/10.1214/08-AOS625

Bickel, P., Buhlmann, P., Yao, Q. W., Samworth, R., Hall, P., Titterington, D. M., … Cook, R. D. (2008). Sure independence screening for ultrahigh dimensional feature space discussion. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 70, 883–911.

Zhang, H. H., & Lu, W. (2007). Adaptive lasso for Cox's proportional hazards model. BIOMETRIKA, 94(3), 691–703. https://doi.org/10.1093/biomet/asm037

Liu, Y., Zhang, H. H., Park, C., & Ahn, J. (2007). Support vector machines with adaptive L-q penalty. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 51(12), 6380–6394. https://doi.org/10.1016/j.csda.2007.02.006

Lu, W., & Zhang, H. H. (2007). Variable selection for proportional odds model. STATISTICS IN MEDICINE, 26(20), 3771–3781. https://doi.org/10.1002/sim.2833

Zhang, H. H., & Lin, Y. (2006). Component selection and smoothing for nonparametric regression in exponential families. Statistica Sinica, 16(3), 1021–1041.

Lin, Y., & Zhang, H. H. (2006). Component selection and smoothing in multivariate nonparametric regression. ANNALS OF STATISTICS, 34(5), 2272–2297. https://doi.org/10.1214/009053606000000722

Zhang, H. H., Ahn, J., Lin, X. D., & Park, C. (2006). Gene selection using support vector machines with non-convex penalty. BIOINFORMATICS, 22(1), 88–95. https://doi.org/10.1093/bioinformatics/bti736

Leng, C., & Zhang, H. H. (2006). Model selection in nonparametric hazard regression. JOURNAL OF NONPARAMETRIC STATISTICS, 18(7-8), 417–429. https://doi.org/10.1080/10485250601027042

Tang, Y., & Zhang, H. H. (2006). Multiclass proximal support vector machines. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 15(2), 339–355. https://doi.org/10.1198/106186006X113647

Zhang, H. H. (2006). Variable selection for support vector machines via smoothing spline ANOVA. Statistica Sinica, 16(2), 659–674.

Ferris, M. C., Voelker, M. M., & Zhang, H. H. (2004). Model building with likelihood basis pursuit. OPTIMIZATION METHODS & SOFTWARE, 19(5), 577–594. https://doi.org/10.1080/1055678042000221719

Zhang, H. H., Wahba, G., Lin, Y., Voelker, M., Ferris, M., Klein, R., & Klein, B. (2004). Variable selection and model building via likelihood basis pursuit. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 99(467), 659–672. https://doi.org/10.1198/016214504000000593