Hanzhong Liu
Biographic Data
| ID | 8921051 |
|---|---|
| NAME | Hanzhong Liu |
| GIVEN NAMES | Hanzhong |
| FAMILY NAME | Liu |
| SIGNATURE | LIU H |
| AFFILIATIONS | Tsinghua University |
| ORCID | 0000-0002-0028-5136 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Rerandomization and Covariate Adjustment in Split-Plot Designs
Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balan…
Design-Based Theory for Lasso Adjustment in Randomized Block Experiments and Rerandomized Experiments
Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regression adjustment method based on the least absolute shrinkage and selection operator (Lasso) to efficiently estimate the average treatment effect in randomized block experiments with high-dimensional covariates. We derive the asymptotic properties of the proposed estimat…
No prominent works on this page.
Rerandomization and Covariate Adjustment in Split-Plot Designs
Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balan…
Design-Based Theory for Lasso Adjustment in Randomized Block Experiments and Rerandomized Experiments
Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regression adjustment method based on the least absolute shrinkage and selection operator (Lasso) to efficiently estimate the average treatment effect in randomized block experiments with high-dimensional covariates. We derive the asymptotic properties of the proposed estimat…
Computer Science (2 works) · Econometrics (2 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Advanced Causal Inference Techniques (1 work) · Advanced Statistical Methods and Models (1 work) · Combinatorics (1 work) · Covariate (1 work) · Optimal Experimental Design Methods (1 work)