Xin Lu and Hanzhong Liu* (2024+). Tyranny-of-the-minority regression adjustment in randomized experiments. Journal of the American Statistical Association, in press.
Hanzhong Liu, Jiyang Ren and Yuehan Yang* (2024). Randomization-based joint central limit theorem and efficient covariate adjustment in randomized block 2K factorial experiments. Journal of the American Statistical Association, 119(545), 136-150.
Xin Lu, Tianle Liu, Hanzhong Liu* and Peng Ding (2023). Design-based theory for cluster rerandomization. Biometrika, 110(2), 467-483.
Hanzhong Liu, Fuyi Tu and Wei Ma* (2023). Lasso-adjusted treatment effect estimation under covariate-adaptive randomization. Biometrika, 110(2), 431-447.
Xinhe Wang#, Tingyu Wang# and Hanzhong Liu* (2023). Rerandomization in stratified randomized experiments. Journal of the American Statistical Association, 118(542), 1295-1304.
Hanzhong Liu and Yuehan Yang* (2020). Regression-adjusted average treatment effect estimates in stratified randomized experiments, Biometrika, 107(4), 935-948.
Adam Bloniarz#, Hanzhong Liu#, Cunhui Zhang, Jasjeet S. Sekhon and Bin Yu* (2016). Lasso adjustments of treatment effect estimates in randomized experiments. Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7383-7390.
Wenqi Shi, Anqi Zhao and Hanzhong Liu* (2024+). Rerandomization and covariate adjustment in split-plot designs. Journal of Business & Economic Statistics, in press.
Ke Zhu, Hanzhong Liu* and Yuehan Yang* (2024+). Design-based theory for Lasso adjustment in randomized block experiments with a general blocking scheme. Journal of Business & Economic Statistics, in press.
Ke Zhu and Hanzhong Liu* (2024). Rejoinder to Reader Reaction “On exact randomization-based covariate-adjusted confidence intervals” by Jacob Fiksel, Biometrics, 80(2), ujae052.
Fuyi Tu, Wei Ma and Hanzhong Liu* (2024). A unified framework for covariate adjustment under stratified randomization, Stat, 13(4), e70016.
Yujia Gu, Hanzhong Liu and Wei Ma* (2023). Regression-based multiple treatment effect estimation under covariate-adaptive randomization. Biometrics, 79(4), 2869-2880.
Ke Zhu and Hanzhong Liu* (2023). Pair-switching rerandomization. Biometrics, 79(3), 2127-2142.
Hanzhong Liu* (2023). Bootstrapping inference of average treatment effect in completely randomized experiments with high-dimensional covariates. Biostatistics & Epidemiology, 6(2), 203-220.
Wei Ma, Fuyi Tu and Hanzhong Liu* (2022). Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective. Statistics in Medicine, 41, 5645-5661.
Ke Zhu and Hanzhong Liu* (2022). Confidence intervals for parameters in high-dimensional sparse vector autoregression. Computational Statistics & Data Analysis, 168, 107383.
Ke Zhu, Yingkai Jiang, Xiang Wang, Zhicheng Shi, Chao Yang*, Hanzhong Liu* and Ke Deng* (2022). A new framework of customized production product certification based on the combination of domain knowledge and data inference (in Chinese). Chinese Journal of Applied Probability and Statistics, 38(4): 581-602.
Hanzhong Liu and Jinzhu Jia* (2022). On estimation error bounds of the Elastic Net when p >> n. Statistics, 56(3), 498-517.
Hanzhong Liu* (2021). Comment on `Inference after covariate-adaptive randomization: aspects of methodology and theory'. Statistical Theory and Related Fields, 5(3), 192-193.
Hanzhong Liu, Xin Xu and Jingyi Jessica Li* (2020). A bootstrap Lasso + Partial Ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models. Statistica Sinica, 30, 1333-1355.
Hanzhong Liu and Bin Yu* (2017). Comments on: High dimensional simultaneous inference with the bootstrap. Test, 26, 740-750.
Lan Wu, Yuehan Yang* and Hanzhong Liu (2014). Nonnegative-lasso and application in index tracking. Computational Statistics & Data Analysis, 70, 116-126.
Hanzhong Liu and Bin Yu* (2013). Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression. Electronic Journal of Statistic, 7, 3124-3169.
Haoyang Yu, Wei Ma and Hanzhong Liu* (2024). Minimax optimal design with spillover and carryover effects, under review.
Tingxuan Han#, Ke Zhu#, Hanzhong Liu* and Ke Deng* (2024). Imputation-based randomization tests for randomized experiments with interference, under review.
Yujia Gu, Hanzhong Liu and Wei Ma* (2024). Incorporating external data for analyzing randomized clinical trials: A transfer learning approach, under review.
Xin Lu#, Hongzi Li# and Hanzhong Liu* (2024). Estimation and inference of average treatment effects under heterogeneous additive treatment effect model, Journal of the Royal Statistical Society, Series B (Statistical Methodology), R&R.
Hongzi Li, Wei Ma, Yingying Ma* and Hanzhong Liu* (2024). Density and treatment effect estimation under covariate-adaptive randomization with heavy-tailed outcomes, under review.
Jiahui Xin, Hanzhong Liu and Wei Ma* (2024). Inference under covariate-adaptive randomization with many strata, under review.
Haoyang Yu, Ke Zhu* and Hanzhong Liu (2024). Sharp variance estimator and causal bootstrap in stratified randomized experiments. Statistics in Medicine, Major revision.
