代表性论文：（#：联合第一作者，*：通讯作者） 

1)       He Z., Sun Y., Liu J.* and Li R. (2024). TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression. Artificial Intelligence and Statistics Conference. https://proceedings.mlr.press/v238/he24a/he24a.pdf

2)       Gao T#, Liu J.#, Pan R.# and Wang H.# (2024). Citation counts prediction of statistical publications based on multi-layer academic networks via neural network model. Expert Systems with Applications. (JCR-1) 238, Part C, 121634.

3)       Guo X.#, Li R.#, Liu J.*# and Zeng M.# (2024). Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators. Journal of Business & Economic Statistics. 42(1), 243-256.

4)       Liu J., Sun A. and Ke Y. (2024). A Generalized Knockoff Procedure for FDR Control in Structural Change Detection. Journal of Econometrics. In press. 239(2), 105331.

5)       Guo X.#, Li R.#, Liu J.*# and Zeng M.# (2023). Statistical Inference for Linear Mediation Models with High-dimensional Mediators and Application to Studying Stock Reaction to COVID-19 Pandemic. Journal of Econometrics. 235, 166-179.

6)       Liao Y.#, Liu J.*#, Coffman D. L.# and Li R.# (2022). Varying Coefficient Mediation Model and Application to Analysis of Behavioral Economics Data. Journal of Business & Economic Statistics. 40(4), 1759-1771.

7)       Guo X.#, Li R.#, Liu J.*# and Zeng M.# (2022). High-dimensional Mediation Analysis for Selecting DNA Methylation Loci Mediating Childhood Trauma and Cortisol Stress Reactivity. Journal of the American Statistical Association. 117(539), 1110-1121.

8)    Liu W.#, Ke Y.*#, Liu J.*# and Li R.# (2022). Model free Feature Screening and FDR Control with Knockoff Features. Journal of the American Statistical Association. 117(537), 428-443.

9)    Wang F., Liu J.* and Wang H. (2021). Sequential Text-Term Selection in Vector Space Models. Journal of Business & Economic Statistics. 39(1), 82-97.

10)    Chu W.#, Li R.#, Liu J.*# and Reimherr M.# (2020). Feature selection for generalized varying coefficient mixed-effect models with application to obesity GWAS. Annals of Applied Statistics. 14(1), 276–298.

11)    Zhou Y.#, Liu J.# and Zhu L. (2020). Test for Conditional Independence with Application to Conditional Screening. Journal of Multivariate Analysis. 175.

12)    Liu J.*, Legg J., Mo. M and Zhang X. (2019). Considerations in testing treatment effects on transient event driven health status changes measured by patient reported outcomes. Statistics in Medicine. 38(29), 5497-5511.

13)    Liu J., Lou L. and Li R. (2018). Variable Selection for Partially Linear Models via Partial Correlation. Journal of Multivariate Analysis. 167, 418–434.

14)    Liu J.*, Ye M., Zhu S., Jiang L., Sang M., Gan J., Wang Q., Huang M. and Wu R. (2018). Two-stage Identification of SNP Effects on Dynamic Poplar Growth. The Plant Journal (JCR-1), 93, 286–296.

15)    Li R.#, Liu J.*# and Lou L.# (2017). Variable Selection via Partial Correlation. Statistica Sinica. 27 (3), 983-996.

16)    Wang L., Liu J.*, Li Y. and Li R. (2017). Model-Free Conditional Independence Feature Screening. Science China Mathematics. 60(3), 551-568.

17)    Liu J.* (2016). Feature Screening and Variable Selection for Partially Linear Models with Ultrahigh- dimensional Longitudinal Data. Neurocomputing. 195, 202-210.

18)    Liu J., Zhong W. and Li R. (2015). A Selective Overview of Feature Screening for Ultrahigh-dimensional Data. Science China Mathematics, 58(10), 1-22.

19)    Jiang L.#, Liu J.#, Zhu X., Ye M., Sun L., Lacaze X. and Wu R. (2015). 2HiGWAS: A Unifying High-Dimensional Platform to Infer the Global Genetic Architecture of Trait Development. Briefings in Bioinformatics, 16(6), 905-911.

20)    Liu J.*, Li R. and Wu R. (2014). Feature Selection for Varying Coefficient Models with Ultrahigh Dimensional Covariates. Journal of the American Statistical Association, 109, 266-274.