AI RESEARCH
Calibrated Principal Component Regression
arXiv CS.LG
•
ArXi:2510.19020v2 Announce Type: replace-cross We propose a new method for statistical inference in generalized linear models. In the overparameterized regime, Principal Component Regression (PCR) reduces variance by projecting high-dimensional data to a low-dimensional principal subspace before fitting. However, PCR incurs truncation bias whenever the true regression vector has mass outside the retained principal components (PC