Variable and subset selection in PLS regression
The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than obtained by different methods. We also present an approach to orthogonal scatter correction. The procedures and comparisons are applied to industrial data. (C) 2001 Elsevier Science B.V. All rights reserved.
variable selection partial least squares (pls) principal component analysis (pca) h-principle stepwise regression orthogonal scatter correction (osc)