On the structure of partial least squares regression

We prove that the two algorithms given in the literature for partial least squares regression are equivalent, and use this equivalence to give an explicit formula for the resulting prediction equation. This in turn is used to investigate the regression method from several points of view. Its relation to principal component regression is clearified, and some heuristic arguments are given to explain why partial least squares regression often needs fewer factors to give its optimal prediction.