In this paper we present a way of conducting design of experiments by Multivariate Additive Partial Least-Squares Splines models, in short MAPLSS. In the framework of optimal experimental design based on small samples, in order to select the most informative MAPLSS model, we process an adaptive incremental selection of observations by a particular bootstrap procedure. WhyMAPLSS models? Because they inherit the advantages of the PLS regression that permits to capture additively non-linear main effects and relevant interactions in the difficult framework of small samples. The effectiveness of this approach is illustrated on the reservoir simulator data used to forecast oil production.
Iterative design of experiments by boosted PLS models. A case study: the reservoir simulator data to forecast oil production
LOMBARDO, Rosaria;
2011
Abstract
In this paper we present a way of conducting design of experiments by Multivariate Additive Partial Least-Squares Splines models, in short MAPLSS. In the framework of optimal experimental design based on small samples, in order to select the most informative MAPLSS model, we process an adaptive incremental selection of observations by a particular bootstrap procedure. WhyMAPLSS models? Because they inherit the advantages of the PLS regression that permits to capture additively non-linear main effects and relevant interactions in the difficult framework of small samples. The effectiveness of this approach is illustrated on the reservoir simulator data used to forecast oil production.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
