8 papers · 1 filter
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…