Hanzhong Liu

发表

Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression

Bin Yu, Hanzhong Liu, 2013, 1306.5505.

Heterogeneous Treatment Effect Estimation through Deep Learning

Hanzhong Liu, Ran Chen, 2018, 1810.11010.

Lasso adjustments of treatment effect estimates in randomized experiments

Cun-Hui Zhang, Bin Yu, Adam Bloniarz, 2015, Proceedings of the National Academy of Sciences.

A general theory of regression adjustment for covariate-adaptive randomization: OLS, Lasso, and beyond

Hanzhong Liu, Wei Ma, Fuyi Tu, 2020, 2011.09734.

Confidence intervals for parameters in high-dimensional sparse vector autoregression

Ke Zhu, Hanzhong Liu, Hanzhong Liu, 2020, Comput. Stat. Data Anal..

Regression-adjusted average treatment effect estimates in stratified and sequentially randomized experiments

Hanzhong Liu, Yuehan Yang, 2019, 1908.01628.

Pair‐switching rerandomization

Hanzhong Liu, K. Zhu, 2021, Biometrics.

Design-based theory for Lasso adjustment in randomized block experiments with a general blocking scheme

Hanzhong Liu, K. Zhu, Yuehan Yang, 2021, 2109.11271.

Design-based theory for Lasso adjustment in randomized block experiments with a general blocking scheme

Hanzhong Liu, K. Zhu, Yuehan Yang, 2021, 2109.11271.

Randomization-based joint central limit theorem and efficient covariate adjustment in randomized block 2K factorial experiments

Hanzhong Liu, Jiyang Ren, Yuehan Yang, 2022, Journal of the American Statistical Association.

Design-based theory for cluster rerandomization

Tianle Liu, P. Ding, Xin Lu, 2022, Biometrika.

Rerandomization and covariate adjustment in split-plot designs

Hanzhong Liu, Anqi Zhao, Wenqi Shi, 2022, 2209.12385.

Regression analysis for covariate‐adaptive randomization: A robust and efficient inference perspective

Hanzhong Liu, Wei Ma, Fuyi Tu, 2020, Statistics in medicine.

A general theory of regression adjustment for covariate-adaptive randomization: OLS, Lasso, and beyond

Hanzhong Liu, Wei Ma, Fuyi Tu, 2020, 2011.09734.

On estimation error bounds of the Elastic Net when p ≫ n

Jinzhu Jia, Hanzhong Liu, 2022, Statistics.

Tyranny-of-the-minority regression adjustment in randomized experiments

Xin Lu, Hanzhong Liu, 2022, 2210.00261.

Testing for individual and time effects in unbalanced panel data models with time-invariant regressors

Hanzhong Liu, Ke Liu, 2022, Electronic Research Archive.

Comments on: High-dimensional simultaneous inference with the bootstrap

Hanzhong Liu, Bin Yu, 2017, TEST.

A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models

Xin Xu, Hanzhong Liu, Jingyi Jessica Li, 2017, Statistica Sinica.

Regression-adjusted average treatment effect estimates in stratified randomized experiments

Hanzhong Liu, Yuehan Yang, 2019, Biometrika.

Comment on ‘Inference after covariate-adaptive randomisation: aspects of methodology and theory’

Hanzhong Liu, 2021, Statistical Theory and Related Fields.

Nonnegative-lasso and application in index tracking

Lan Wu, Hanzhong Liu, Yuehan Yang, 2014, Comput. Stat. Data Anal..

Randomization-based joint central limit theorem and efficient covariate adjustment in stratified $2^K$ factorial experiments

Hanzhong Liu, Yuehan Yang, Jiyang Ren, 2021, 2103.04050.

Comments on: High-dimensional simultaneous inference with the bootstrap

Bin Yu, Hanzhong Liu, 2017 .

Rerandomization in Stratified Randomized Experiments

Tingyu Wang, Hanzhong Liu, Xinhe Wang, 2020, Journal of the American Statistical Association.

Randomization-based joint central limit theorem and efficient covariate adjustment in stratified 2 factorial experiments

Hanzhong Liu, Yuehan Yang, Jiyang Ren, 2021 .

Bootstrapping inference of average treatment effect in completely randomized experiments with high-dimensional covariates

Hanzhong Liu, Hanzhong Liu, 2021, Biostatistics & Epidemiology.

Penalized regression adjusted causal effect estimates in high dimensional randomized experiments

Hanzhong Liu, Yuehan Yang, 2018, 1809.08732.