  Methods based on partial least squares (PLS) regression, which has recently
gained much attention in the analysis of high-dimensional genomic datasets,
have been developed since the early 2000s for performing variable selection.
Most of these techniques rely on tuning parameters that are often determined by
cross-validation (CV) based methods, which raises important stability issues.
To overcome this, we have developed a new dynamic bootstrapbased method for
significant predictor selection, suitable for both PLS regression and its
incorporation into generalized linear models (GPLS). It relies on the
establishment of bootstrap confidence intervals, that allows testing of the
significance of predictors at preset type I risk $\alpha$, and avoids the use
of CV. We have also developed adapted versions of sparse PLS (SPLS) and sparse
GPLS regression (SGPLS), using a recently introduced non-parametric
bootstrap-based technique for the determination of the numbers of components.
We compare their variable selection reliability and stability concerning tuning
parameters determination, as well as their predictive ability, using simulated
data for PLS and real microarray gene expression data for PLS-logistic
classification. We observe that our new dynamic bootstrapbased method has the
property of best separating random noise in y from the relevant information
with respect to other methods, leading to better accuracy and predictive
abilities, especially for non-negligible noise levels.
  Keywords: Variable selection, PLS, GPLS, Bootstrap, Stability
