| Abstract: | In the literature, much effort has been put into modeling dependence among variables and their interactions through nonlinear transformations of predictive variables. In this paper, we propose a nonlinear generalization of Partial Least Squares (PLS) using multivariate additive splines. We discuss the advantages and drawbacks of the proposed model, building it via the generalized cross validation criterion (GCV) criterion, and show its performance on a real dataset and on simulated datasets in comparison to other methods based on splines. Copyright © 2009 John Wiley & Sons, Ltd. |
