12 papers
A Tutorial on Bregman Projection in Statistics
Gunhee Cho, Jae Kwang Kim, Yumou Qiu
A single geometric operation -- projecting a reference onto a constrained family under a Bregman divergence -- underlies a striking range of statistical methods. This tutorial deve…
Statistical Optimality of Prediction-Powered Inference
Se Yoon Lee, Jae Kwang Kim
The prediction-powered inference (PPI) proposed by Angelopoulos et al. (2023) is a popular method that leverages a small number of labeled samples and machine learning predictions…
TS-Neyman: Posterior Sampling for Adaptive Stratified Estimation
Kosuke Morikawa, Mst Moushumi Pervin, Jae Kwang Kim
Many model evaluation tasks reduce to estimating an average loss, error rate, or subgroup metric on a stratified pool when each label, human rating, or simulator call is costly. Th…
MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation
Se Yoon Lee, Yonghyun Kwon, Jae Kwang Kim
Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-even…
MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation
Se Yoon Lee, Jae Kwang Kim
Obtaining high-quality labels is costly, whereas unlabeled covariates are often abundant, motivating semi-supervised inference methods with reliable uncertainty quantification. Pre…
Sample-split REGression SREG: A robust estimator for high-dimensional survey data
Yonghyun Kwon, Shu Yang, Jae Kwang Kim
Model-assisted regression estimation is fundamental in survey sampling for incorporating auxiliary information. However, when the auxiliary dimension grows with the sample size, th…
A Calibration Framework for Inference with Partially Observed Data
Mst Moushumi Pervin, Hengfang Wang, Jae Kwang Kim
Missing data is an universal problem in statistics. We develop a unified framework for estimating parameters defined by general estimating equations under a missing-at-random (MAR)…
Bregman projection for calibration estimation in Survey Sampling
Jae Kwang Kim, Yonghyun Kwon, Yumou Qiu
Calibration weighting is a fundamental tool in survey sampling for incorporating auxiliary population information into design-based estimators. Classical formulations measure dista…
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…
Generalized entropy calibration for analyzing voluntary survey data
Yonghyun Kwon, Jae Kwang Kim, Yumou Qiu
Statistical analysis of voluntary survey data is an important area of research in survey sampling. We consider a unified approach to voluntary survey data analysis under the assump…
Debiased calibration estimation using generalized entropy in survey sampling
Yonghyun Kwon, Jae Kwang Kim, Yumou Qiu
Incorporating the auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a popular tool for incorporating the auxili…