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Principal Component Regression Dominates all Monotone Spectral Filters for Linear Regression

arXiv:2609.39440v1 Announce Type: new Abstract: We compare the instance-wise, finite-sample risks of monotone spectral filters for linear regression, a broad class of estimators including principal component regression (PCR), gradient descent (GD), and ridge regression. We show that PCR dominates a…

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  1. 2026-10-01 04:00 · arXiv stat.ML
    Principal Component Regression Dominates all Monotone Spectral Filters for Linear Regression

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