On overfitting and post-selection uncertainty assessments

• 成果类型: Article
• 署名作者: Hong, L.; Kuffner, T. A.; Martin, R.
• 署名单位: Robert Morris University; Washington University (WUSTL); North Carolina State University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx083
• 发表日期: 2018
• 页码: 221224
• 关键词: model

摘要

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 submodel, may not be valid because it ignores the selected submodel's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.