Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features.

ABSTRACT: Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift are common. While most research focuses on model estimation, there is limited literature on transfer inference for model accuracy despite its importance. We introduce a novel Doubly Robust Augmented Model Accuracy Transfer InferenCe (DRAMATIC) method for point and interval estimation of commonly used classification performance measures in an unlabeled target population with labeled source data. DRAMATIC derives and evaluates a potentially misspecified risk model for a binary response, leveraging high-dimensional adjustment features from bot

SUBMITTER: Zhou D 

PROVIDER: S-EPMC12037143 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature