Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression
The aim of this paper is to suggest a bootstrap-based method for choosing the number of components in Partial Least Squares Regression (PLSR). Cross-validated Qh2 statistic is used, for which is intended to derive a bootstrap distribution and to perform a hypothesis testing. Monte Carlo approximation is adopted. Applications on both artificial and real data are presented. Lien vers l'article
AMATO, S. and ESPOSITO VINZI, V. (2003). Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression. Chemometrics and Intelligent Laboratory Systems, 68(43862), pp. 5-16.
Mots clés : #Variable-and-Component-Selection, #RegressionPartial-Least-Squares-Regression, #Irrelevant-factors, #Bootstrap, #Cross, #validation