Publications

Published Papers

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

  1. Feng X., Gao, Y., Huang, J., Jiao, Y. and Liu, X.* (2024). A Relative Entropy Gradient Sampler for Unnormalized Distributions. Journal of Computational and Graphical Statistics. Accepted. http://arxiv.org/abs/2110.02787. Python-code “REGS” is available at https://github.com/xliusufe/REGS.

  2. Chen, Z., Cheng, X., and Liu, X.* (2023). Hypothesis testing on high dimensional quantile regression. Journal of Econometrics. 238. Published online. DOI:10.1016/j.jeconom.2023.105525.

  3. Zhang, X., Liu, X., and Shi X.* (2023). Model Selection for Varying Coefficient Nonparametric Transformation Model. The Econometrics Journal. 26, 492-512. DOI:10.1093/ectj/utad007. An R-package “GFabs” is available at https://github.com/xliusufe/GFabs.

  4. 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.

  5. Bar-Lev, S. K., Batsidis, A., Einbeck, J.*, Liu, X. and Ren, P. (2023). Cumulant-Based Goodness-of-fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions. Mathematics. 11, 1603. DOI:10.3390/math11071603.

  6. 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.

  7. 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.

  8. Hu, J., Liu, X.*, Liu, X. and Xia, N. (2022). Some aspects of response variable selection and estimation in multivariate linear regression. Journal of Multivariate Analysis. 188, 104821. DOI: 10.1016/j.jmva.2021.

  9. 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.

  10. 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.

  11. Gao, B.$^{\natural}$, Liu, X. $^{\natural}$, Li, H. and Cui, Y.* (2020). 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.

  12. 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.

  13. Liu, X., Shi, X. and Jiang, H. (2018). Ultra-high dimensional variable selection for piecewise linear loss functions. Manuscript. An R-package “plvs” is available at https://github.com/xliusufe/plvs.

  14. Liu, X., Gao, B. and Cui, Y.* (2017). Generalized partially linear varying multi-index coefficient model for gene-environment interactions. Statistical Applications in Genetics and Molecular Biology. 16, 59-74.

  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., Song, X., Xie, S.* and Zhou, Y. (2016). Variable selection for gamma frailty transformation models with application to diabetic complications. Canadian Journal of Statistics. 44, 375-394.

  17. Liu, X., Wang, H. and Cui, Y.* (2016). Statistical identification of gene-gene interactions triggered by nonlinear environmental modulation. Current Genomics. 17, 388-395.

  18. Luo, T., Liu, X. and Cui, Y.* (2016). A genome-wide association analysis in four populations reveals strong genetic heterogeneity for birth weight. Current Genomics. 17, 416-426.

  19. Sa, J., Liu, X., He, T., Liu, G. and Cui, Y.* (2016). A nonlinear model for gene-based gene-environment interaction. International Journal of Molecular Sciences. 17, 882.

  20. 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.

  21. Zhao, M., Jiang, H. and Liu, X. (2013). A note on estimation of the mean residual life function with left-truncated and right-censored data. Statistics and Probability Letters. 83, 2332-2336.

  22. Liu, X.*, Liu, P. and Zhou, Y. (2011). Distribution estimation with auxiliary information for missing data. Journal of Statistical Planning and Inference. 141, 711-724.

  23. Liu, X.* and Ishifaq, A. (2011). Distribution estimation with smoothed auxiliary information. ACTA Mathematicae Applicatae Sinica. 27, 167-176.

  24. Li, J., Liu, X. and Zhu, J.* (2009). $r$-Dominating set problem and $k$-Center problem in weighted trees. OR Transaxtions. 13, 111-118.

Working Papers

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