On overfitting and post-selection uncertainty assessments
Statistics Theory
2017-12-08 v1 Applications
Methodology
Statistics Theory
Abstract
In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected sub-model, may not be valid because it ignores the selected sub-model's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.
Cite
@article{arxiv.1712.02379,
title = {On overfitting and post-selection uncertainty assessments},
author = {Liang Hong and Todd A. Kuffner and Ryan Martin},
journal= {arXiv preprint arXiv:1712.02379},
year = {2017}
}