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We analyse the prediction error of principal component regression (PCR) andprove non-asymptotic upper bounds for the corresponding squared risk. Undermild assumptions, we show that PCR performs as well as the oracle methodobtained by replacing empirical principal components by their populationcounterparts. Our approach relies on upper bounds for the excess risk ofprincipal component analysis.

A note on the prediction error of principal component regression