A note on the variance in principal component regression

Published 4 Jan 2023 in stat.ME, math.ST, and stat.TH | (2301.01543v3)

Abstract: Principal component regression results in lack of fit when important dimensions are omitted, which cannot be assessed from the eigenvalues. I show that the PC-regression estimator can also suffer from increased variance relative to ordinary least squares in such cases.

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Bert van der Veen

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