Partial Least Squares Regression

PLSR—or PLSR1—is a regression method for collinear data, and can be seen as a competitor to principal component regression.

Abstract

Abstract Partial least squares regression was introduced as an algorithm in the early 1980s, and it has gained much popularity in chemometrics. PLSR—or PLSR1—is a regression method for collinear data, and can be seen as a competitor to principal component regression. The method makes it possible to combine prediction with a study of a joint latent structure in the x , y ‐variables. PLSR2 is a generalization to several dependent variables. PLSR1 can be understood from a statistical point of view via its parameter algorithm and the corresponding population model.