Bootstrap selection of Multivariate Additive PLS Spline models


Abstract


En
Multivariate Additive PLS Splines, in short MAPLSS, are Partial Least-Squares models that study the dependence of a set of responses on spline transformations of the predictor variables which permit to capture additively non linear main effects and interactions. The aim of this paper is to present a way of selecting MAPLSS models through an adaptive incremental selection of training samples by a bootstrap procedure. This approach is attractive in the case of expensive data thus implying to construct efficient models based on small training data sets.

Keywords: Bootstrap; PLS regression; B-splines; Design of experiments

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