Publications

Published Papers

($\color{red}{\small{*corresponding author,\underline{trainee}}} $)

  1. Xu, C., Su, H., Liu, X. and You, J.* (2026). Detecting Structural Breaks In High-Dimensional Functional Time Series Factor Models. Statistica Sinica. DOI:10.5705/ss.202025.0014.

  2. 刘旭，谭祥勇. (2025). 高维数据分析与统计推断，科学技术出版社（专著）. ISBN 978-7-03-083320-4.

  3. Liu, X., Huang, J., Zhou, Y., Zhang, F. and Ren, P.* (2025). Subgroup testing in change-plane models and its applications to medical data. Statistica Sinica. DOI:10.5705/ss.202025.0155.

  4. Tan, X., Zhang, X., Cui, Y. and Liu, X.* (2024). Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis. Bioinformatics. 40(9). An R-package “gcdl” is available at https://github.com/XiaoZhangryy/gcdl.

  5. Liu, X., Lian, H.* and Huang, J. (2024). More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization. Journal of Machine Learning Research. 25, 1-27. An R-package “tensorMAM” is available at https://github.com/xliusufe/tensorMAM.

  6. Feng X., Gao, Y., Huang, J., Jiao, Y. and Liu, X.* (2025). Relative Entropy Gradient Sampler for Unnormalized Distributions. Journal of Computational and Graphical Statistics. 34(1), 211-221. http://arxiv.org/abs/2110.02787. Python-code “REGS” is available at https://github.com/xliusufe/REGS.

  7. Chen, Z., Cheng, X., and Liu, X.* (2024). Hypothesis testing on high dimensional quantile regression. Journal of Econometrics. 238.

  8. Zhang, X., Shi X.*, Liu, Y., Liu, X., and Ma, S. (2023). A general framework for identifying hierarchical interactions and its application to genomics data. Journal of Computational and Graphical Statistics. 32, 873-883. DOI:10.1080/10618600.2022.2152034. An R-package “HierFabs” is available at https://github.com/xliusufe/HierFabs.

  9. Hu, J., Huang, J., Liu, X. and Liu, X.* (2023). Response Best-subset Selector for Multivariate Regression with High-dimensional Response Variables. Biometrika. 110, 205-223. DOI:10.1093/biomet/asac037. An R-package “rbs” is available at https://github.com/xliusufe/rbs.

  10. Li, X., Feng, X. and Liu, X.* (2022). Heritability estimation for a linear combination of phenotypes via ridge regression. Bioinformatics. 38, 4687-4696. DOI:10.1093/bioinformatics/btac587. An R-package “MultiRidgeVar” is available at https://github.com/xg-SUFE1/MultiRidgeVar.

  11. Cheng, C., Feng, X., Huang, J. and Liu, X.* (2022). Regularized projection score estimation of treatment effects in high-dimensional quantile regression. Statistica Sinica. 32, 23-41. DOI: 10.5705/ss.202019-0247. An R-package “pqr” is available at https://github.com/xliusufe/pqr.

  12. Liu, X., Zheng, S. and Feng, X.* (2020). Estimation of error variance via ridge regression. Biometrika. 107, 481-488. An R-package “RidgeVar” is available at https://github.com/xliusufe/RidgeVar, and a Python package “ridgevar” is available https://github.com/xliusufe/RidgeVarpy. DOI: 10.1093/biomet/asz074.

  13. Gao, B.$^{\natural}$, Liu, X. $^{\natural}$, Li, H. and Cui, Y.* (2019). Integrative analysis of genetical genomics data incorporating network structures. Biometrics. 75, 1063-1075. ($^{\natural}$The first two authors contributed equally to this work) DOI: 10.1111/biom.13072. An R-package “IVGC” is available at https://github.com/xliusufe/IVGC.

  14. Liu, X., Zhong, P. S. and Cui, Y.* (2020). Joint test of parametric and nonparametric effects in partial linear models for gene-environment interaction. Statistica Sinica. 30, 325-346. DOI: 10.5705/ss.202017.0039.

  15. Liu, X., Cui, Y.* and Li, R. (2016). Partially linear varying multi-index coefficient model for integrative gene-environment interactions. Statistica Sinica. 26, 1037-1060.

  16. Liu, X., Jiang, H.* and Zhou, Y. (2014). Local empirical likelihood inference for varying-coefficient density-ratio models based on case-control data. Journal of the American Statistical Association. 109, 635-646.

Working Papers

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