A new Universal Resample Stable Bootstrap-based Stopping Criterion in PLS Components Construction

Abstract: We develop a new robust stopping criterion in Partial Least Squares Regressions (PLSR) components construction characterised by a high level of stability. This new criterion is defined as a universal one since it is suitable both for PLSR and its extension to Generalized Linear Regressions (PLSGLR). This criterion is based on a non-parametric bootstrap process and has to be computed algorithmically. It allows to test each successive components on a preset significant level alpha. In order to assess its performances and robustness with respect to different noise levels, we perform intensive datasets simulations, with a preset and known number of components to extract, both in the case n>p (n being the number of subjects and p the number of original predictors), and for datasets with n<p. We then use t-tests to compare the performance of our approach to some others classical criteria. The property of robustness is particularly tested through resampling processes on a real allelotyping dataset. Our conclusion is that our criterion presents also better global predictive performances, both in the PLSR and PLSGLR (Logistic and Poisson) frameworks.