  Recently, many regularized procedures have been proposed for variable
selection in linear regression, but their performance depends on the tuning
parameter selection. Here a criterion for the tuning parameter selection is
proposed, which combines the strength of both stability selection and
cross-validation and therefore is referred as the prediction and stability
selection (PASS). The selection consistency is established assuming the data
generating model is a subset of the full model, and the small sample
performance is demonstrated through some simulation studies where the
assumption is either held or violated.

Methodology;Machine Learning