Quantile treatment effects and bootstrap inference under covariate-adaptive randomization
In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive the...
Saved in:
Main Authors: | ZHANG, Yichong, ZHENG, Xin |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/soe_research/2453 https://ink.library.smu.edu.sg/context/soe_research/article/3452/viewcontent/1370_6907_1_PB.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Quantile treatment effects and bootstrap inference under covariate-adaptive randomization
by: ZHENG, Xin, et al.
Published: (2018) -
Bootstrap inference for quantile treatment effects in randomized experiments with matched pairs
by: JIANG, Liang, et al.
Published: (2024) -
Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations
by: JIANG, Liang, et al.
Published: (2021) -
Extremal quantile treatment effects
by: ZHANG, Yichong
Published: (2018) -
Wild bootstrap for instrumental variable regressions with weak and few clusters
by: WANG, Wenjie, et al.
Published: (2021)