部分发表论文：（#：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)       Li Y., Bi J., Liu J. and Yang Y. (2024). A supervised weeding method to cluster high dimensional predictors with application to job market analysis. Journal of Applied Statistics. In press. DOI: 10.1080/02664763.2024.2348634.  

3)       Liu J.*, Liao Y. and Li R. (2024). Generalized Varying Coefficient Mediation Models. Communications in Mathematics and Statistics. In press. DOI: 10.1007/s40304-023-00366-2.

4)       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. 238, Part C, 121634.

5)       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.

6)       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.

7)       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.

8)       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.

9)       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.

10)    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.

11)    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.

12)    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.

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

14)    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.

15)    Zhou Y., Liu J.*, Hao Z. and Zhu L. (2019). Model-Free Conditional Feature Screening with Exposure Variables. Statistics and Its Interface. 12, 239-251.

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

17)    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, 93, 286–296.

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

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

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

21)    Shi L., He Q., Liu J. and He Z. (2016). A Modified Region Approach for Multivariate Measurement System Capability Analysis. Quality and Reliability Engineering International. 32(1), 37-50.

22)    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.

23)    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.

24)    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.

25)    Fu, G., Liu, J., Luo, J., Wang, Z., Wang, Y., Wang, N. and Wu, R. (2013). Book chapter: Systems Mapping: A Computational Tool for Personalized Medicine. Handbook of Personalized Medicine. Jenny Stanford Publishing.

26)    Liu J., Wang Z., Wang Y., Li R. and Wu R. (2012). Model and Algorithm for Linkage Disequilibrium Analysis in a Nonequilibrium Population. Frontiers in Statistical Genetics. 3, 78.

27)    Wang Z., Liu J., Wang J., Wang Y., Wang N., Li Y., Li R. and Wu R. (2012). Dynamic Modeling of Genes Controlling Cancer Stem Cell Proliferation. Frontiers in Statistical Genetics. 3, 84.

28)    Das K., Huang Z., Liu J., Fu G., Li J., Li Y., Tong C., Gai J. and Wu R. (2012). Functional Mapping of Developmental Processes: Theory, Applications, and Prospects. Methods in Molecular Biology. 871, 227-243.

29)    Li J., Das K., Liu J., Fu G., Li Y., Tobias C. and Wu R. (2012). Statistical Models for Genetic Mapping in Polyploids: Challenges and Opportunities. Methods in Molecular Biology. 871, 245-261.

30)    Hou W., Sui Y., Wang Z., Wang Y., Wang N., Liu J., Li Y., Goodenow M., Yin L., Wang Z. and Wu R. (2012). Systems mapping of HIV-1 infection. BMC Genetics. 13(1), 91-97.