Selected Journal Papers

2009

• Nam-Hee Choi, Ji Zhu, Yan Sun and Kerby Shedden (2009) Penalized regression methods for ranking multiple genes by their strength of unique association with a quantitative trait.

• Jian Guo, Liza Levina, George Michailidis and Ji Zhu (2009) Pairwise variable selection for high-dimensional model-based clustering. (One of four winning papers in the 2009 ASA Student Paper Competition sponsored by the Statistical Computing Section)

• Gareth James, Chiara Sabatti, Nengfeng Zhou and Ji Zhu (2009) Sparse regulation networks.

• Adam Rothman, Liza Levina and Ji Zhu (2009) A new approach to Cholesky-based covariance regularization in high dimensions.

• Jie Peng, Ji Zhu, Anna Bergamaschi, Wonshik Han, Dong-Young Noh, Jonathan Pollack and Pei Wang (2009) Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer.

2008

• Lacey Gunter, Ji Zhu and Susan Murphy (2009) Variable selection for qualitative interactions. Statistical Methodology. Accepted. (Special issue on data mining)

• Jian Guo, Gareth James, Liza Levina, George Michailidis and Ji Zhu (2008) Principal component analysis with sparse fused loadings.

• Liza Levina and Ji Zhu (2008) Discussion of "Sure independence screening for ultra-high dimensional feature space" by J. Fan and J. Lv. Journal of the Royal Statistical Society, Series B.

• Jie Peng, Pei Wang, Nengfeng Zhou and Ji Zhu (2008) Partial correlation estimation by joint sparse regression models. Journal of the American Statistical Association. Accepted.

• Adam Rothman, Liza Levina and Ji Zhu (2008) Generalized thresholding of large covariance matrices. Journal of the American Statistical Association. Accepted.

• Sijian Wang, Bin Nan, Saharon Rosset and Ji Zhu (2008) Random lasso.

• Sijian Wang, Bin Nan, Nengfeng Zhou and Ji Zhu (2008) Hierarchically penalized Cox regression for censored data with grouped variables and its oracle property. Biometrika. Accepted. (Winner of the 2008 ICSA J.P. Hsu Memorial Award)

• Hui Zou, Ji Zhu and Trevor Hastie (2008) New multi-category boosting algorithms based on multi-category Fisher-consistent losses. Annals of Applied Statistics 2:1290-1306.

2007

• Youjuan Li and Ji Zhu (2007) Analysis of array CGH data for cancer studies using the fused quantile regression. Bioinformatics 23:2470-2476.

• Adam Rothman, Peter Bickel, Liza Levina and Ji Zhu (2008) Sparse permutation invariant covariance estimation. Electronic Journal of Statistics 2:494-515. (One of four winning papers in the 2008 ASA Student Paper Competition sponsored by the Statistical Computing Section)

• Yaoyun Shi, Noah Stoffman, Kathy Yuan and Ji Zhu (2007) Predicting stock returns and fund performance through network analysis.

• Li Wang and Ji Zhu (2007) Financial market forecasting using a two-step kernel learning method for the support vector regression. Annals of Operations Research. Accepted. (Special issue on data mining)

• Li Wang and Ji Zhu (2007) Image denoising via solution paths. Annals of Operations Research. Accepted. (Special issue on data mining)

• Li Wang, Ji Zhu and Hui Zou (2008) Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics 24:412-419.

• Hao Zhang, Yufeng Liu, Yichao Wu and Ji Zhu (2008) Variable selection for multi-category SVM via adaptive sup-norm regularization. Electronic Journal of Statistics 2:149-167.

• Nengfeng Zhou and Ji Zhu (2007) Group variable selection via a hierarchical lasso and its oracle property.

2006

• Nam-Hee Choi, William Li and Ji Zhu (2006) Variable selection with the strong heredity constraint and its oracle property. (One of the winning papers in the 2007 ENAR Student Paper Competition)

• Trevor Hastie and Ji Zhu (2006) Discussion of "Support vector machines with applications" by J. Moguerza and A. Munoz. Statistical Science 21:352-357.

• Gareth James, Jing Wang and Ji Zhu (2006) Functional linear regression that's interpretable. Annals of Statistics. Accepted.

• Liza Levina, Adam Rothman and Ji Zhu (2008) Sparse estimation of large covariance matrices via a nested lasso penalty. Annals of Applied Statistics 2:245-263.

• Sijian Wang, Bin Nan, Ji Zhu and David Beer (2008) Doubly penalized Buckley-James method for survival data with high-dimensional covariates. Biometrics 64:132-140. (Winner of the 2007 ENAR John van Ryzin Award)

• Sijian Wang and Ji Zhu (2007) Improved centroids estimation for the nearest shrunken centroid classifier. Bioinformatics 23:972-979. (One of four winning papers in the 2007 ASA Student Paper Competition sponsored by the Statistical Computing Section)

• Sijian Wang and Ji Zhu (2008) Variable selection for model-based high-dimensional clustering and its application to microarray data. Biometrics 64:440-448.

• Haijun Yang, Byron Roe and Ji Zhu (2007) Studies of stability and robustness for artificial neural networks and boosted decision trees. Nuclear Instruments and Methods for Physics Research, Section A 574:342-349.

2005

• Lacey Gunter and Ji Zhu (2007) Efficient computation and model selection for the support vector regression. Neural Computation 19:1633-1655.

• Youjuan Li, Yufeng Liu and Ji Zhu (2007) Quantile regression in reproducing kernel Hilbert spaces. Journal of the American Statistical Association 102:255-268.

• Youjuan Li and Ji Zhu (2008) L1-norm quantile regression. Journal of Computational and Graphical Statistics 17:163-185. (One of four winning papers in the 2006 ASA Student Paper Competition sponsored by the Statistical Computing Section)

• Li Wang, Ji Zhu and Hui Zou (2006) The doubly regularized support vector machine. Statistica Sinica 16:589-615. (Special issue on data mining and machine learning)

• Haijun Yang, Byron Roe and Ji Zhu (2005) Studies of boosted decision trees for MiniBooNE particle identification. Nuclear Instruments and Methods for Physics Research, Section A 555:370-385.

• Ji Zhu, Hui Zou, Saharon Rosset and Trevor Hastie (2009) Multi-class adaboost. Statistics and Its Interface. Accepted. (Special issue on data mining and machine learning)

2004

• Trevor Hastie, Saharon Rosset, Rob Tibshirani and Ji Zhu (2004) The entire regularization path for the support vector machine. Journal of Machine Learning Research 5:1391-1415.

• Byron Roe, Haijun Yang, Ji Zhu, Yong Liu, Ion Stancu and Gordon McGregor (2005) Boosted decision trees as an alternative to artificial neural networks for particle identification. Nuclear Instruments and Methods for Physics Research, Section A 543:577-584.

• Saharon Rosset and Ji Zhu (2007) Piecewise linear regularized solution paths. Annals of Statistics 35:1012-1030.

• Saharon Rosset, Ji Zhu and Trevor Hastie (2004) Boosting as a regularized path to a maximum margin classifier. Journal of Machine Learning Research 5:941-973.

• Rob Tibshirani, Michael Saunders, Saharon Rosset, Ji Zhu and Keith Knight (2005) Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society, Series B 67:91-108.

2003

• Jerry Friedman, Trevor Hastie, Saharon Rosset, Rob Tibshirani and Ji Zhu (2004) Discussion of three boosting papers on "Consistency in boosting". The three papers are by (1) W. Jiang (2) G. Lugosi, N. Vayatis and (3) T. Zhang. Annals of Statistics 32:102-107.

• Saharon Rosset and Ji Zhu (2004) Discussion of "Least angle regression" by B. Efron, T. Hastie, I. Johnstone and R. Tibshirani. Annals of Statistics 32:469-475.

• Ji Zhu and Trevor Hastie (2004) Classification of gene microarrays by penalized logistic regression. Biostatistics 5:427-443.

• Ji Zhu and Trevor Hastie (2005) Kernel logistic regression and the import vector machine. Journal of Computational and Graphical Statistics 14:185-205. (One of four winning papers in the 2002 ASA Student Paper Competition sponsored by the Statistical Computing Section)

Selected Refereed Conference Papers

• Saharon Rosset, Nathan Srebro, Grzegorz Swirszcz and Ji Zhu (2007) L1 regularization in infinite dimensional feature spaces. The Annual Conference on Learning Theory 20.

• Saharon Rosset, Ji Zhu, Hui Zou and Trevor Hastie (2005) A method for inferring label sampling mechanisms in semi-supervised learning. The Annual Conference on Neural Information Processing Systems 17.

• Saharon Rosset, Ji Zhu and Trevor Hastie (2004) Margin maximizing loss functions. The Annual Conference on Neural Information Processing Systems 16.

• Ji Zhu, Saharon Rosset, Trevor Hastie and Rob Tibshirani (2004) 1-norm support vector machines. The Annual Conference on Neural Information Processing Systems 16. (One of the spotlight papers at NIPS 2003)