A note on selection stability: combining stability and prediction
Methodology
2013-01-31 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1301.7118,
title = {A note on selection stability: combining stability and prediction},
author = {Yixin Fang and Junhui Wang and Wei Sun},
journal= {arXiv preprint arXiv:1301.7118},
year = {2013}
}