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Preprints

• Jin, W., Wu, G., and Li, Q. (2024+). Inference on the Significance of Modalities in Multimodal Generalized Linear Models.
• Jiang, J., Li, Q., Lin, J., and Lin, F. (2024+). Classification of Competing Risks under a Semiparametric Density Ratio Model with Transition of Markers.
• Liu, T., Li, Q., Zheng, X., and Zou, F. (2024+). Adaptive Regularized Tri-factor Non-negative Matrix Factorization for Cell Type Deconvolution.
• Yang, Y., Lin, J., Li, Q., and Lin, F. (2024+). Maximum Likelihood Estimation for the Silent Hypnozoite Carriage in a Malaria Randomized Clinical Trial.

Statistical Theory & Methodology

• Heiling, H., Rashid, N., Li, Q., Peng, X., Yeh, J., and Ibrahim, J. (2024+). Efficient Computation of High-dimensional Penalized Piecewise Constant Hazard Random Effects Survival Models. Statistics in Medicine, in press.
• Wang, P., Wang, H., Li, Q., Shen, D., and Liu, Y. (2024). Joint and Individual Component Regression. Journal of Computational and Graphical Statistics, 33, 763-773. [pdf] [supp]
• Heiling, H., Rashid, N., Li, Q., and Ibrahim, J. (2024). glmmPen: High Dimensional Penalized Generalized Linear Mixed Models. The R Journal, 15, 106-128. [pdf]
• Heiling, H., Rashid, N. Li, Q., Peng, X., Yeh, J., and Ibrahim, J. (2024). Efficient Computation of High-Dimensional Penalized Generalized Linear Mixed Models by Latent Factor Modeling of the Random Effects. Biometrics, 80: ujae016. [pdf] [supp] [code]
• Wang, H., Li, Q., and Liu, Y. (2024). Multi-response Regression for Block-missing Multi-modal Data without Imputation, Statistica Sinica, 34: 527-546. [pdf] [supp]
• Chen, J., Li, Q., and Chen, H.Y. (2023). Testing Generalized Linear Models with High-dimensional Null Hypothesis. Biometrika, 110, 83-99. [pdf] [supp]
• Liu, Y., Darville, T., Zheng, X., and Li, Q. (2023). Decomposition of Variation of Mixed Variables by a Latent Mixed Gaussian Copula Model. Biometrics, 2, 1187-1200. [pdf] [supp]
• Yang, H., Lin, D.Y., and Li, Q. (2023). An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis. Statistica Sinica, 33, 1-22. [pdf] [supp]
• Wang, P., Li, Q., Shen, D., and Liu, Y. (2023). High-Dimensional Factor Regression for Heterogeneous Subpopulations. Statistica Sinica, 33, 1-27. [pdf] [supp]
• Wang, H., Li, Q., and Liu, Y. (2023). Adaptive Supervised Learning on Data Streams in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint. Stat, 12:e514. [pdf]
• Wang, H., Li, Q., and Liu, Y. (2022). Regularized Buckley-James Method for Right-censored Outcomes with Block-missing Multi-modal Covariates. Stat, 11:e515. [pdf]
• Jiang, H., Li, Q., Lin, J., and Lin, F. (2022). Classification of Disease Recurring using Transition Likelihoods with EM Algorithm. Statistics in Medicine, 41, 4697-4715. [pdf] [supp]
• Li, Q. and Li, L. (2022). Integrative Factor Regression and Its Inference for Multimodal Data Analysis. Journal of the American Statistical Association, 117, 2207-2221. [pdf] [supp]
• Liu, Y., Lin, J., Lin, F., and Li, Q. (2022). Dynamic Classification of Plasmodium vivax Malaria Recurrence: An Application of Classifying Unknown Cause of Failure in Competing Risks. Journal of Data Science, 20, 51-78. [pdf] [supp]
• Lin, F., Li, Q., and Lin, J. (2020). Relapse or Reinfection: Classification of Malaria Infection Using Transition Likelihoods. Biometrics, 76, 1351-1363. [pdf] [supp]
• Rashid, N., Li, Q., Yeh, J., and Ibrahim, J. (2020). Modeling Between-Study Heterogeneity for Reproducible Gene Signature Selection and Clinical Prediction. Journal of the American Statistical Association, 115, 1125–1138. [pdf] [supp]
• 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, 1406–1419. [pdf] [supp]
• Li, X., Li, Q., Zeng, D., Marder, K., Paulsen, J., and Wang, Y. (2020). Time-varying Hazards Model Incorporating Irregularly Measured High-dimensional Biomarkers. Statistica Sinica, 30, 1605–1632. [pdf] [supp]
• Ta, T., Shao, J., Li, Q., and Wang, L. (2020). Generalized Regression Estimators with High-Dimensional Covariates. Statistica Sinica, 30, 1135–1154. [pdf] [supp]
• Li, Q. and Li, L. (2018). Integrative Linear Discriminant Analysis with Guaranteed Error Rate Improvement. Biometrika, 105, 917-930. [pdf] [supp]
• Avella-Medina, M., Battey, H., Fan, J., and Li, Q. (2018). Robust Estimation of High Dimensional Covariance and Precision Matrices. Biometrika, 105, 271-284. [pdf] [supp]
• Li, Q., Cheng, G., Fan, J., and Wang, Y. (2018). Embracing the Blessing of Dimensionality in Factor Models. Journal of the American Statistical Association, 113, 380-389. [pdf] [supp]
• Li, Q., Yu, M., and Wang, S. (2017). A Statistical Framework for Pathway and Gene Identification from Integrative Analysis. Journal of Multivariate Analysis, 156, 1-17. [pdf]
• Fan, J., Li, Q., and Wang, Y. (2017). Estimation of High-Dimensional Mean Regression in Absence of Symmetry and Light-tail Assumptions. Journal of the Royal Statistical Society: Series B, 79, 247-265. [pdf] [supp]
• Li, Q. and Shao, J. (2015). Regularizing LASSO: A Consistent Variable Selection Method. Statistica Sinica, 25, 975-992. [pdf] [supp]
• Li, Q. and Shao, J. (2015). Sparse Quadratic Discriminant Analysis for High Dimensional Data. Statistica Sinica, 25, 457-473. [pdf] [supp]
• Xu, Y., Yu, M., Zhao, Y. Q., Li, Q., Wang, S., and Shao, J. (2015). Regularized Outcome Weighted Subgroup Identification for Differential Treatment Effects. Biometrics, 71, 645-653. [pdf] [supp] [code] {An earlier version received 2014 John Van Ryzin Award.}
• Li, Q., Wang, S., Huang, C., Yu, M., and Shao, J. (2014). Meta-Analysis Based Variable Selection for Gene Expression Data. Biometrics, 70, 872-880. [pdf] [supp]
• Yu, M. and Li, Q. (2014). Discussion of “Combining Biomarkers to Optimize Patient Treatment Recommendations”. Biometrics, 70, 716-719. [pdf]