Data Science - Research, Education, and Outreach, UA-TRIPODS
Nonparametrics, smoothing splines
High dimensiaonal data analysis, feature selection, sparse methods
Statisical machine learning, classification
Biological, biomedical data analysis
Shin, S., Wu, J. Zhang, H. H. and Liu, Y. (2017)
Principal weighted support vector machines for sufficient dimension reduction in binary classification,
Biometrika, 104 (1):67-81.
Hao, N., Feng, Y. and Zhang, H. H.. (2017)
Model Selection for High Dimensional Quadratic Regression via Regularization,
Journal of American Statistical Association, forthcoming.
Kong, D., Xue, K., Yao, F. and Zhang, H. H. . (2016)
Partially functional linear regression in high dimensions,
Biometrika, 103: 147-159.
Glazer, E., Zhang, H. H. , Hill, K., Patel, C., Kha, S., Yozwiak, M., Bartels, H., Watkins, J., Alberts, D., and
Krouse, R. (2016)
Evaluating IPMN and pancreatic carcinoma utilizing quantitative histopathology.
Cancer Medicine, 5: 2841-2847.
Geng, Y., Zhang, H. H. , and Lu, W. (2015)
On optimal treatment regimes selection for mean survival time.
Statistics in Medicine, 34: 1169-1184.
Ma, C., Zhang, H. H., and Wang, X. (2014)
Machine learning for big data analytics in plants.,
Trends in Plance Science, 19:798-808.
Hao, N. and Zhang, H. H.. (2014)
Interaction screening for ultra-high dimensional data.
Journal of American Statistical Association, 109: 1285-1301.
Caner, M. and Zhang, H. H. . (2014)
Adaptive elastic net for generalized methods of moments.
Journal of Business & Economic Statistics, 32: 30-47.
Zhu, H., Yao, F., and Zhang, H. H. (2014)
Structured functional additive regression in reproducing kernel Hilbert spaces.
Journal of the Royal Statistical Society, Series B. , 76: 581-603.
Lu, W., Zhang, H. H., and Zeng. D. (2013)
Variable selection for optimal treatment decision.
Statistical Methods in Medical Research , 22(5): 492-503.
Liu, Y., Zhang, H. H. , and Wu, Y. (2011)
Hard or soft or classification? Large-margin unified machines.
Journal of the American Statistical Association , 106: 166-177.
Zhang, H. H., Cheng, G. and Liu, Y. (2011)
Linear or nonlinear? Automatic structure discovery for partially linear models. Journal of American Statistical Association , 106: 1099-1112.
Wu, Y., Zhang, H. H., and Liu, Y. (2010)
Robust model-free multiclass probability estimation.
Journal of American Statistical Association , 105: 424-436.
Qiao, X., Zhang, H. H. , Liu, Y., Todd, M. and Marron, S. (2010)
Weighted distance weighted discrimination and its asymptotic properties.
Journal of American Statistical Association, 105: 401-414.
Lu, W. and Zhang, H. H. (2010)
On estimation of partially linear transformation models.
Journal of American Statistical Association, 105: 683-691.
Zou, H. and Zhang, H. H. (2009)
On the adaptive elastic-net with a diverging number of parameters.
Annals of Statistics, 37: 1733-1751.
Clarke, B., Fokoue, E., and Zhang, H. H. (2009)
Principles and Theory for Data Mining and Machine Learning,
Zhang, H. H. , Liu, Y., Wu, Y. and Zhu, J. (2008)
Variable selection for multicategory SVM via sup-norm regularization,
Electronic Journal of Statistics, 2:149-167.
Zhang, H. H. (2008)
Support Vector Machine Classification for High Dimensional Microarray Data Analysis, with Applications in Cancer Research. In High-Dimensional Data Analysis in Cancer Research,Li and Xu (eds),
Zhang, H. H. and Lu, W. (2007)
Adaptive-LASSO for Cox's proportional hazard model.
Biometrika, 93: 1-13.
Zhang, H. H., Ahn, J., Lin, X., and Park, C. (2006)
Gene selection using support vector machines with nonconvex penalty.
Bioinformatics, 22: 88-95.
Zhang, H. H. (2006)
Variable selection for Support vector machines via smoothing spline ANOVA.
Statistica Sinica, 16: 659-674.
Lin, Y. and Zhang, H. H. (2006)
Component selection and smoothing in multivariate nonparametric regression.
Annals of Statistics, 34: 2272-2297.
Tang, Y. and Zhang, H. H. (2005)
Multiclass proximal support vector machines.
Journal of Computational and Graphical Statistics, 15:339-355.
Zhang, H. H., Wahba, G., Lin, Y., Voelker, M., Ferris, Klein, R. and Klein, B.
Variable selection and model building via likelihood basis pursuit.
Journal of American Statistical Association, 99: 659-672.
Wahba, G., Lin, Y., Lee, Y. and Zhang, H. H. (2003)
Optimal properties and adaptive tuning of standard and nonstandard support vector machines.
In Nonlinear Estimation and Classification , Springer, 125-143.
Lin, Y., Wahba, G., Zhang, H. H., and Lee, Y. (2002)
Statistical properties and adaptive tuning of support vector machines.
Machine Learning , 48: 115-136.