4 papers · 1 filter
Multi-source Learning for Target Population by High-dimensional Calibration
Haoxiang Zhan, Jae Kwang Kim, Yumou Qiu
Multi-source learning is an emerging area of research in statistics, where information from multiple datasets with heterogeneous distributions is combined to estimate the parameter…
A General Approach for Calibration Weighting under Missing at Random
Yonghyun Kwon, Jae Kwang Kim, Yumou Qiu
We propose a unified class of calibration weighting methods based on weighted generalized entropy to handle missing at random (MAR) data with improved stability and efficiency. The…
Collaborative Indirect Treatment Comparisons with Multiple Distributed Single-arm Trials
Yuru Zhu, Huiyuan Wang, Haitao Chu +2
When randomized controlled trials are impractical or unethical to simultaneously compare multiple treatments, indirect treatment comparisons using single-arm trials offer valuable…
Multiply Robust Inference of Average Treatment Effects by High-dimensional Empirical Likelihood
Xintao Xia, Yumou Qiu
In this paper, we develop a multiply robust inference procedure of the average treatment effect (ATE) for data with high-dimensional covariates. We consider the case where it is di…