Publications


    • Li, J., Yu., G., Li, Q., and Liu, Y. Sample-wise Combined Missing Effect Models with Penalization. Journal of Computational and Graphical Statistics, to appear.
    • Fu, S., Chen, P., Liu, Y., and Ye., Z. Simplex-based Multinomial Logistic Regression with Diverging Numbers of Categories and Covariates. Statistica Sinica, to appear.
    • Qi, Z.,  Pang, J., and Liu, Y. On Robustness of Individualized Decision Rules.  Journal of the American Statistical Association, to appear.
    • Qi, Z., Cui, Y., Liu, Y., and Pang, J. S. Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization. Mathematics of Operations Research, to appear.
    • Wang, X.,  Liu, B.,  Zhang, X., and Liu, Y. Efficient Multiple Change Point Detection for High-dimensional Generalized Linear Models. The Canadian Journal of Statistics, to appear.
    • Wang, P., Li, Q., Shen, D., and Liu, Y. High-Dimensional Factor Regression for Heterogeneous Subpopulations. Statistica Sinica, to appear.
    • Luo, Y. and Liu, Y. Recovery of Sums of Sparse and Dense Signals by Incorporating Graphical Structure Among Predictors. The Canadian Journal of Statistics, to appear.
    • Fan, Y., Lu, X., Liu, Y., and Zhao, J. Angle-based hierarchical classification using exact label embedding. Journal of the American Statistical Association, to appear.

    • Mo, W. and Liu, Y. (2022). Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models. Journal of the Royal Statistical Society, Series B, 84, 2, 440-472.
    • Liu, J., Wang, H., Sun, W., and Liu, Y. (2022). Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data. Journal of the American Statistical Association, 117:537, 38-51.
    • Liu, B., Zhang, X., and Liu, Y. (2022). High Dimensional Change Point Inference: Recent Developments and Extensions.  Journal of Multivariate Analysis, 188, 104833.
    • Yu, G., Fu, H., and Liu, Y. (2022). High-dimensional Cost-constrained Regression via Nonconvex Optimization. Technometrics, 64, 1, 52–64.
    • Fan, Y., Lu, X., Zhao, J. Fu, H., and Liu, Y.  (2022). Estimating individualized treatment rules for treatments with hierarchical structure.  Electronic Journal of Statistics, 16, 1, 737–784.

    • Liu, B., Zhang, X., and Liu, Y. (2021). Simultaneous Change Point Detection and Structure Recovery for High Dimensional Gaussian Graphical Models.  Journal of Machine Learning Research, 22, 274, 1–62.
    • Mo, W., Qi, Z., and Liu, Y. (2021). Learning Optimal Distributionally Robust Individualized Treatment Rules. Journal of the American Statistical Association, 116, 534, 659–674. Rejoinder: 116, 534, 699–707. This paper appeared in JASA as a discussion paper (with discussion and our rejoinder).
    • Rashid, N. U., Luckett, D. J., Chen, J., Lawson, M. T.,Wang, L., Zhang, Y., Laber, E. B., Liu, Y., Yeh J. J., Zeng, D., and Kosorok, M. R. (2021). High dimensional precision medicine from patient-derived xenografts. Journal of the American Statistical Association, 116:535, 1140-1154.
    • Cheng, Q., Argon, N. T., Evans, C. S. Liu, Y., Platts-Mills, T. F., and Ziya, S. (2021). Forecasting emergency department hourly occupancy using time series analysis. The American Journal of Emergency Medicine, 48, 177-182.
    • Yang, J., Liu, Y., Liu, Y., and Sun, W. (2021). Model Free Estimation of Graphical Model using Gene Expression Data. Annals of Applied Statistics, 15, 1, 194-207.
    • Chen, J., Tran-Dinh, Q., Kosorok , M. R., and Liu, Y. (2021). Identifying Heterogeneous Effect using Latent Supervised Clustering with Adaptive Fusion. Journal of Computational and Graphical Statistics, 30, 1, 43-54.
    • Pham, M., Ninh, A., Le, H., and  Liu, Y. (2021). An Efficient Algorithm for Multi-term Nonsmooth Objective Functions.  Journal of Computational and Graphical Statistics, 30, 1, 162-170.
    • Liu, L. Y., Liu, Y., and Zhu, H. (2021).  MCNN: Masked Convolutional Neural Network for Supervised Learning Problems, STAT, 9, 1, e290.
    • Gong, S., Zhang, K., and Liu, Y. (2021). Penalized linear regression with high-dimensional pairwise screening. Statistica Sinica, 31, 391-420.

    • Yu, G., Li, Q., Shen, D., and Liu, Y.  (2020). Optimal Sparse Linear Prediction for Block-missing Multi-modality Data without Imputation. Journal of the American Statistical Association, 115, 531, 1406-1419.
    • Zhang, C., Chen, J., Fu, H., He, X., Zhao, Y., and Liu, Y. (2020). Multicategory Outcome Weighted Margin-based Learning for Estimating Individualized Treatment Rules. Statistica Sinica, 20, 4, 1857-1879.
    • Liu, B., Zhou, C., Zhang, X., and Liu, Y. (2020). A Unified Data-adaptive Framework for High Dimensional Change Point Detection. Journal of the Royal Statistical Society, Series B, 82, 4, 933-963.
    • Yu, G., Liang, Y., Lu, S. and Liu, Y.  (2020). Confidence Intervals for Sparse Penalized Regression. Journal of the American Statistical Association, 115, 530, 794-809.
    • Qi, Z., Liu, D., Fu, H., and Liu, Y. (2020). Multi-armed Angle-based Direct learning for Estimating Optimal Individual Treatment Rules with Treatment Scores. Journal of the American Statistical Association, 115, 530, 678-691.
    • Shin, S., Liu, Y., Cole, S. R., and Fine, J. P. (2020). Ensemble maximum likelihood estimation and variable selection with semiparametric regression models. Biometrika, 107, 2, 433-444.
    • Pritchard, D. and Liu, Y. (2020). Composite Quantile-Based Classifiers.  Statistical Analysis and Data Mining, 13, 4, 337-353.

    • Qi, Z., Cui, Y., Liu, Y., and Pang, J. (2019). Estimation of Individualized Decision Rules Based on An Optimized Covariate-dependent Equivalent of Random Outcomes. SIAM Journal on Optimization, 29(3), 2337–2362.
    • Qian, C., Tran-Dinh, Q., Fu, S., Zou, C. and Liu, Y. (2019). Robust Multicategory Support Matrix Machine. Mathematical Programming, 176, 1–2, 429–463.
    • Liu, J., Sun, W. and Liu, Y. (2019). Joint Skeleton Estimation of Multiple Directed Acyclic Graphs for Heterogeneous Population. Biometrics, 75, 1, 36–47.
    • Wang, P., Liu, Y., and Shen, D. (2019). Flexible Locally Weighted Penalized Regression with Applications on Prediction of Alzheimer’s Disease Neuroimaging Initiative’s Clinical Scores. IEEE Transactions on Medical Imaging, 36, 6, 1398–1408.
    • Qi, Z. and Liu, Y. (2019). Convex Bidirectional Large Margin Classifiers. Technometrics, 61, 2, 176-186.
    • Liu, J., Yu, G. and Liu, Y. (2019). Graph-based Sparse Linear Discriminant Analysis for High Dimensional Classification. Journal of Multivariate Analysis, 171, 250-269.
    • Fu, S., He, Q., Zhang, S., and Liu, Y. (2019). Robust Outcome Weighted Learning for Optimal Individualized Treatment Rules. Journal of Biopharmaceutical Statistics, 29, 4, 606–624.

    • Qi, Z. and Liu, Y. (2018). D-learning to estimate optimal individual treatment rules. Electronic Journal of Statistics, 12, 2, 3601-3638.
    • Chen, J., Fu, H., He, X., Kosorok, M. R. and Liu, Y. (2018). Estimating Individualized Treatment Rules for Ordinal Treatments. Biometrics, 74, 924-933.
    • Chen, J., Zhang, C., Kosorok, M. R. and Liu, Y. (2018). Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction (with discussion and our rejoinder). Statistics and Its Interface, 11, 401-431.
    • Zhao, J., Yu, G., and Liu, Y. (2018). Angle Breakdown Point for Classification. Annals of Statistics, 46, 6B, 3362-3389.
    • Sun, W. W., Hao, B., Liu, Y., and Cheng, G. (2018). Simultaneous Clustering and Estimation of Multiple Graphical Models. Journal of Machine Learning Research, 18, 217, 1-58.
    • Zhang, C., Pham, M., Fu, S., and Liu, Y. (2018). Robust Multicategory Support Vector Machines using Difference Convex Algorithm. Mathematical Programming, 169, 1, 277-305.
    • Gong, S., Zhang, K., and Liu, Y. (2018). Efficient Testing-based Variable Selection for High-dimensional Linear Models. Journal of Multivariate Analysis, 166, 17-31.
    • Fu, S., Zhang, S., and Liu, Y. (2018). Adaptively weighted large margin angle-based classifiers. Journal of Multivariate Analysis, 166, 282-299.
    • Liu, L. Y., Liu, Y., and Zhu, H. (2018). SMAC: Spatial Multi-category Angle-based Classifier for High-dimensional Neuroimaging Data. Neuroimage, 175, 230-245.
    • Sun, W., Cheng, G., and Liu, Y. (2018). Large-Margin Classifier Selection via Decision Boundary Instability. Statistica Sinica, 28, 1-25.

    • Kimes, P., Liu, Y., Hayes, D. N., and Marron, J. S. (2017).  Statistical Significance for Hierarchical Clustering. Biometrics, 73, 3, 811-821.
    • Shin, S. J.,  Wu, Y.,  Zhang, H. H.,  and Liu, Y.  (2017). Weighted Principal Support Vector Machine for Sufficient Dimension Reduction in Binary Classification. Biometrika, 104, 67-81.
    • Lu, S., Liu, Y., Liang, Y., and Zhang, K. (2017). Confidence Intervals and Regions for the LASSO using Stochastic Variational Inequality Techniques in Optimization. Journal of the Royal Statistical Society, Series B, 79, 589-611.
    • Xiao X., Liu, X., Lu, X., Chang, X., and Liu, Y. (2017). Solution Path for Reinforced Multicategory Support Vector Machines. Canadian Journal of Statistics, 45, 149-163.
    • Zhang, C., Lu, X., Zhu, Z., Hu, Y., Singh, D., Jones, C., Liu, J., Prins, J. F., and Liu, Y. (2017). REC: Fast Sparse Regression-based Multicategory Classification. Statistics and Its Interface, 10, 2, 175-185.
    • Kirkpatrick, C.,  Broberg, C., McCool, E., Lee, W. J.,  Chao, A., McConnell, E.,Pritchard, D.,  Hebert, M., Fleeman, R, Adams, J., Jamil, A.,  Madera, L., Strömstedt, A.,  Goransson, U., Liu, Y., Hoskin, D., Shaw, L., and  Hicks, L. (2017). The `PepSAVI-MS’ pipeline for natural product bioactive peptide discovery. Analytical Chemistry, 89, 2, 1194-1201.

    • Xie, Y., Liu, Y., and Valdar, W. (2016). Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genetics. Biometrika103, 3, 493-511.
    •  Yu, G. and Liu, Y. (2016). Sparse Regression Incorporating Graphical Structure among Predictors. Journal of the American Statistical Association, 111, 514, 707-720.
    •  Zhang, C., Liu, Y., Wang, J., and Zhu, H. (2016). Reinforced Angle-based Multicategory Support Vector Machines. Journal of Computational and Graphical Statistics, 25, 3, 806-825.
    • Yu, G., Liu, Y., and Shen, D. (2016). Graph Guided Joint Prediction of Class Label and Clinical Scores for the Alzheimer’s Disease.  Brain Structure and Function, 221, 7, 3787-801.
    • Kimes, P., Hayes, D. N., Marron, J. S., and Liu, Y. (2016). Large-Margin Classification with Multiple Decision Rules. Fast Sparse Regression-based Multicategory Classification. Statistical Analysis and Data Mining, 9, 2, 89-105.
    • Chen, G., Liu, Y., Shen, D., and Kosorok, M. R. (2016). Composite Large Margin Classifiers with Latent Subclasses for Heterogeneous Biomedical Data. Statistical Analysis and Data Mining, 9, 2, 75-88.
    • Shin, S., Fine, J., and Liu, Y. (2016). Adaptive Estimation with Partially Overlapping Models. Statistica Sinica, 26, 235-253.
    • Zhang, C., Liu, Y. and Wu, Y. (2016). On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint. Journal of Machine Learning Research, 17, 40, 1-45.

    • Sun, W., Liu, Y., Crowley, 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., and Pardo-Manuel de Villena, F. (2015). IsoDOT Detects Differential RNA-isoform Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity. Journal of the American Statistical Association, 110, 511, 975-986.
    • Huang, H., Liu, Y., Yuan, M., and Marron, J. S. (2015). Statistical significance of clustering through soft thresholding. Journal of Computational and Graphical Statistics, 24, 4, 975-993.
    • Lee, W. and Liu, Y. (2015). Estimation of Multiple Graphical Models with Common Structures. Journal of Machine Learning Research, 16, 1035-1062.
    • The Cancer Genome Atlas Research Network. (2015). Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature, 517, 576–582.
    • Sun, Q., Zhu, H., Liu, Y., Ibrahim, J. G. (2015). SPReM: Sparse Projection Regression Model for high-dimensional linear regression. Journal of the American Statistical Association, 110, 509, 289-302.

    • Kimes, P., Cabanski, C., Wilkerson, M., Zhao, N., Johnson, A., Perou, C., Makowski, L., Maher, C., Liu, Y., Marron, J. S., Hayes, D. N. (2014). SigFuge: single gene clustering of RNA-seq reveals differential isoform usage among cancer samples. Nucleic Acids Research, doi: 10.1093/nar/gku521.
    • Shin, S. J., Wu, Y., Zhang, H. H., and Liu, Y. (2014). Probability-enhanced sufficient dimension reduction for binary classification. Biometrics, 70, 546-555.
    • Kruppa*, J., Liu*, Y., Biau, G., Kohler, M., Konig, I. R., Malley, J. D., and Ziegler, A. (2014). Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory. Biometrical Journal, 56, 4, 534-563 (with discussion).
    • Kruppa, J., Liu, Y., Diener, H. C., Holste, T, Weimar, C., Konig, I. R., and Ziegler, A. (2014). Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications. Biometrical Journal, 56, 4, 564-583 (with discussion).
    • Zhang, C. and Liu, Y. (2014). Multicategory Angle-based Large-margin Classification. Biometrika, 101(3), 625-640.
    • An, B., Guo, J. and Liu, Y. (2014). Hypothesis Testing for Band Size Detection of High Dimensional Banded Precision Matrices. Biometrika, 101, 2, 477-483.
    • Yu, G., Liu, Y., Thung, K-H. and Shen, D. (2014). Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals. PLoS ONE 9(5): e96458.
    • Qiao, X., Liu, Y. and Marron, J.S. (2014). Significance Analysis for Pairwise Variable Selection in Classification, Statistics and Its Interface, 7, 263–274.
    • Burgel, R-R, Paillasseur, J-L, Dusser, D., Roche, N., Liu, D., Liu, Y., Furtwaengler, A., Metzdorf, N., and Decramer, M. (2014). Tiotropiummight improve survival in subjects with COPD at high risk of mortality. Respiratory Research, 15:64.

    • Huang, H., Liu, Y., Du, Y., Perou, C., Hayes, D. N., Todd, M., and Marron, J. S. (2013). Multiclass distance weighted discrimination. Journal of Computational and Graphical Statistics, 22, 4, 953-969.
    • Lee, M. H. and Liu, Y. (2013). Kernel Continuum Regression. Computational Statistics and Data Analysis, 68, 190-201.
    • Zhang, C. and Liu, Y. (2013). Multicategory Large-margin Unified Machines, Journal of Machine Learning Research, 14, 1349-1386.
    • Zhang, C., Liu, Y., and Wu, Z. (2013). On the effect and remedies of shrinkage on classification probability estimation. The American Statistician, 67, 3, 134-142.
    • Hu, Y., Huang, Y.,  Du, Y., Orellana, C., Singh, D., Kuan, P., Scott, R., Scott, H., Chiang, D., Hayes, N., Jones, C.,  Liu, Y.,  Prins, J., and Liu, J. (2013). DiffSplice: the Genome-Wide Detection of Differential Splicing Events with RNA-seq.  Nucleic Acids Research, 41(2):e39.
    • Wu, Y. and Liu, Y. (2013). Adaptively weighted large margin classifiers. Journal of Computational and Graphical Statistics, 22, 2, 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, 2, 379-395.
    • Wang, P., Dong, Q., Zhang. C., Kuan. P.F., Liu, Y., Jeck, W.R., Andersen, J.B., Jiang W, Savich GL, Tan TX, Auman JT, Hoskins JM, MisherAD, Yourstone YM, Kim JW, Cibulskis K, Getz G, Hunt HV, Thorgeirsson SS, Roberts LR, Ye D, Guan KL, Xiong Y, Qin LX, Chiang DY.  (2013). Mutations in isocitrate dehydrogenase 1 and occur frequently in intrahepatic cholangiocarcinomas and share hypermethylationtargets with glioblastomas. Oncogene, 32(25), 3091-3100.
    • Huang, Y., Hu, Y., Jones, C. D., MacLeod, J. N., Chiang, D. Y., Liu, Y., Prins, J. F., and Liu, J. (2013). A robust method for transcript quantification with RNA-seq data. Journal of Computational Biology, 20(3), 167-187.
    • Janssens, W., Liu, Y., Liu, D., Kesten, S., Tashkin, D. P., Celli, B. R., Decramer, M. (2013). Quality and reproducibility of spirometry in COPD patients in a randomized trial (UPLIFT®), Respiratory Medicine, 107, 9, 1409-1416.

    • Lee, W., Du., Y., Sun, W., Hayes, D. N., and Liu, Y. (2012). Multiple response regression for Gaussian mixture models with known labels.Statistical Analysis and Data Mining, 5, 6, 493-508.
    • Huang, H., Liu, Y., and Marron, J. S. (2012). Bi-directional discrimination with application to data visualization. Biometrika, 99, 4, 851-864.
    • The Cancer Genome Atlas Research Network. (2012). Comprehensive genomic characterization of squamous cell lung cancers. Nature, 489, 519-525.
    • Huang, H., Lu, X., Liu, Y., Haaland, P., and Marron, J. S. (2012). R/DWD: Distance weighted discrimination for classification, visualization and batch adjustment. Bioinformatics, 28, 8, 1182-1183.
    • Lee, W. and Liu, Y. (2012). Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood. Journal of Multivariate Analysis, 111, 241-255.

    • Zhang, H. H., Cheng, G. and Liu, Y. (2011). Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models. Journal of the American Statistical Association, 106, 495, 1099-1112.
    • Liu, Y. and Yuan, M. (2011). Reinforced multicategory support vector machines. Journal of Computational and Graphical Statistics, 20, 4, 901–919.
    • Samarov, D., Marron, J.S., Liu, Y., Grulke, C., and Tropsha, A. (2011). Local kernel canonical correlation analysis with application to virtual drug screening. Annals of Applied Statistics, 5, 3, 2169-2196.
    • Singh D., Orellana C., Hu Y., Jones C. D., Liu Y., Chiang D., Liu J., Prins J. F. (2011). FDM: A Graph-based Statistical Method to Analyze Differential Transcription using RNA-seq data. Bioinformatics, 27, 2633-2640.
    • 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, 2, 415-437.
    • Ang, M. K., Patel, M. R.,  Yin, X. Y.,  Fritchie, K.,  Zhao, N., Liu, Y., Wilkerson, M.,  Weissler, M. C.,  Shockley, W.,  Couch,  M. E., Zanation, A. M.,   Hackman, T.,  Chera, B.,   Harris,  S. L.,  Miller,  C. R., Thorne,  L. B., Hayward, M. C.,  Funkhouser, W. K.,  Olshan, A. F.,  Shores, C. G., and Hayes, D. N. (2011). High XRCC1 expression is associated with poorer survival in patients with head and neck squamous cell carcinoma. Clinical Cancer Research, 17, 20, 6542-6552.
    • Fan, C., Prat, A., Parker, J., Liu, Y., Carey, L., Troester, M., and Perou, C. (2011). Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures. BMC Medical Genomics, 4:3, 1-15.
    • Park, S. Y. and Liu, Y. (2011). Robust penalized logistic regression with truncated loss. The Canadian Journal of Statistics, 39, 2, 300-323.
    • 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.

    • Qiao, X., Zhang, H. H., Liu, Y., Todd, M. J., and Marron, J. S. (2010). Weighted distance weighted discrimination and its asymptotic properties. Journal of the American Statistical Association, 105, 489, 401-414.
    • Park, S. Y., Liu, Y., Liu, D., and Scholl, P. (2010).  Multicategory composite least-squares classifiers. Statistical Analysis and Data Mining, 3, 4, 272-286.
    • Wu, Y., Zhang, H. H., and Liu, Y. (2010). Robust Model-free Multiclass Probability Estimation. Journal of the American Statistical Association, 105, 489, 424-436.
    • Wilkerson, M. D.,  Yin, X.,  Hoadley, K. A.,  Liu, Y., Hayward, M. C.,  Miller, C. R.,  Randell, S. H.,  Socinksi, M.,  Parsons, A. M., Funkhouser, W. K., Lee, C.,  Roberts, P.,   Thorne, L.,  Bernard, P. S., Perou, C. M.,  and Hayes, D. N. (2010). Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically-important and correspond to different normal cell types. Clinical Cancer Research, 16, 4864-4875.
    • Liu, Y., Wu, Y., and He, Q. (2010). Utility-based weighted multicategory robust Support Vector Machines. Statistics and Its Interface, 3, 465-476.

    • Zhu, Z. and Liu, Y. (2009). Estimating spatial covariance using penalized likelihood with weighted L1 penalty. Journal of Nonparametric Statistics, 21, 7, 925-942.
    • Qiao, X. and Liu, Y. (2009). Adaptive weighted learning for unbalanced multicategory classification. Biometrics, 65, 159-168.
    • Wu, Y. and Liu, Y. (2009). Stepwise multiple quantile regression estimation using non-crossing constraints. Statistics and Its Interface, 2, 299-310.
    • Park, S. Y. and Liu, Y. (2009). From the support vector machine to the bounded constraint machine. Statistics and Its Interface, 2, 285-298.
    • Wu, Y. and Liu, Y. (2009). Variable selection in quantile regression. Statistica Sinica, 19, 801-817.

    • Liu, Y., Hayes, D. N., Nobel, A., and Marron, J. S. (2008). Statistical significance of clustering for high dimension low sample size data. Journal of the American Statistical Association, 103, 483, 1281-1293.
    • 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.
    • Wang, J., Shen, X., and Liu, Y. (2008). Probability estimation for large margin classifiers. Biometrika, 95, 1, 149-167.

    • Liu, Y. (2007). Fisher consistency of multicategory support vector machines. Eleventh International Conference on Artificial Intelligence and Statistics, 289-296.
    • 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.
    • Wu, Y. and Liu, Y. (2007)Robust truncated-hinge-loss support vector machines. Journal of the American Statistical Association, 102, 479, 974-983.
    • Li, Y., Liu, Y., and Zhu, J. (2007). Quantile regression in Reproducing Kernel Hilbert Spaces. Journal of the American Statistical Association, 102, 477, 255-268.
    • Liu, Y., Ruan, S., and Dean, A. M. (2007). Construction and analysis of Es­2 efficient supersaturated designsJournal of Statistical Planning and Inference, 137, 5,1516-1529.
    • Liu, Y., Zhang, H. H., Park, C., and Ahn, J. (2007). Support Vector Machines with adaptive Lq penalties. Computational Statistics and Data Analysis, 51, 12, 6380-6394.
    • Alcorta, D., Barnes, D. A.,  Dooley, M. A., Sullivan, P., Jonas, B., Liu, Y., Lionaki, S., Reddy, C. B., Chin, H., Dempsey, A. A., Jennette, J. C., and Falk, R. J. (2007). Leukocyte Gene Expression Signatures in Antineutrophil Cytoplasmic Autoantibody (ANCA) and Lupus Glomerulonephritis, Kidney International, 72, 853-864.

    • Liu, Y. and Wu, Y. (2006). Optimizing psi-learning via mixed integer programming. Statistica Sinica, 16, 2, 441-457.
    • Liu, Y. and Shen, X. (2006). Multicategory psi-learning. Journal of the American Statistical Association, 101, 474, 500-509.

    • Liu, Y., Shen, X., and Doss, H. (2005). Multicategory psi-learning and support vector machine: computational tools. Journal of Computational and Graphical Statistics, 14, 1, 219-236.

    • Liu, Y. and Dean, A. M. (2004). k-circulant supersaturated designsTechnometrics, 46, 1, 32-43.