Shrinkage Structure of Partial Least Squares

Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This characterization is useful in providing a link between PLS and other shrinkage estimators, such as principal components regression (PCR) and ridge regression (RR), thus facilitating a direct comparison of PLS with these methods. This paper gives a detailed analysis of the shrinkage structure of PLS, and several new results are presented regarding the nature and extent of shrinkage.     

Mergers and acquisitions in the light of 1992 — Ensuring strategic good sense underpins corporate ambitions

The prospect of executing acquisitions in Europe currently generates more excitement than almost any other item on the corporate agenda. Venturing out of the home market is regarded once more as a test of management virility.Yet without sound strategic analysis coupled with a careful review of management capacity, ventures abroad are highly risky and may represent poor value for shareholders. This paper returns to the fundamentals required for a methodical review of the merits of expansion by acquisition in unfamiliar territory. It concludes that it is important to marry the theoretical analysis with the most careful commercial review.