Brief

Researchers have introduced Calibrated Principal Component Regression (CPCR), a novel method designed to improve statistical inference in generalized linear models, particularly within overparameterized scenarios. CPCR addresses the truncation bias inherent in standard Principal Component Regression by learning a low-variance prior in the principal component subspace and then calibrating the model in the original feature space. Theoretical analysis and empirical results indicate that CPCR outperforms traditional PCR by effectively managing truncation bias and enhancing prediction accuracy across various overparameterized problems. AI