  We analyse the prediction error of principal component regression (PCR) and
prove non-asymptotic upper bounds for the corresponding squared risk. Under
mild assumptions, we show that PCR performs as well as the oracle method
obtained by replacing empirical principal components by their population
counterparts. Our approach relies on upper bounds for the excess risk of
principal component analysis.
