Sai Li. Debiasing the debiased Lasso with bootstrap. Electronic Journal of Statistics, 14(1): 2298-2337,2020.
Sai Li, T. Tony Cai, and Hongzhe Li. Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach. Journal of the American Statistical Association. 117(540): 1835-1846, 2022.
Jianqiao Wang, Sai Li, and Hongzhe Li. A unified approach to robust inference for genetic covariance. Journal of the American Statistical Association(accepted). 2023.
Sai Li, T. Tony Cai, and Hongzhe Li. Statistical Inference for High Dimensional Regression with Proxy Data. Statistica Sinica (accepted).2024.
Zhanrui Cai, Sai Li, Xintao Xia, Linjun Zhang. Private Estimation and Inference in High-Dimensional Regression with FDR Control. Submitted. 2023
Sai Li, T. Tony Cai, and Hongzhe Li. Transfer learning for high-dimensional linear regression: Prediction, estimation, and minimax optimality. Journal of the Royal Statistical Society Series B, 84: 149–173, 2022.
Sai Li, T. Tony Cai, and Hongzhe Li. Transfer learning in large-scale graphical models with false discovery rate control. Journal of the American Statistical Association, 118(543): 2171–2183. 2023.
Sai Li, Tianxi Cai, and Rui Duan. Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach. Annals of Applied Statistics, 17(4): 2970-2992. 2023.
Sai Li, Linjun Zhang, T. Tony Cai, and Honghze Li. Estimation and inference in high-dimensional generalized linear models with knowledge transfer. Journal of the American Statistical Association(accepted). 2023.
Sai Li and Linjun Zhang. Multi-dimensional domain generalization with low-rank structures. Journal of the American Statistical Association(accepted). 2025.
Sai Li and Ting Ye. A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data. Journal of the Royal Statistical Society Series B. 87(2), 529-548. 2025.
Sai Li and Zijian Guo. Causal inference for nonlinear outcomes with possibly invalid instrumentalvariables. in revision. 2020.
Sai Li. Mendelian Randomization when many instruments are invalid: hierarchical empirical Bayes estimation. Technical report. 2017.
Sai Li, Yisha Yao, and Cun-Hui Zhang. Comment: A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models Journal of the American Statistical Association 118(543):1586-1589. 2023.
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn. Improving out-of-distribution robustness via selective augmentation. International Conference of Machine Learning. 2022.
Sai Li, Ritwik Mitra, and Cun-Hui Zhang. Comment: An adaptive resampling test for detecting the presence of significant predictors. Journal of the American Statistical Association. 110(512): 1455-1456. 2016.