Sequential selection procedures and false discovery rate control

• 成果类型: Article
• 署名作者: G'Sell, Max Grazier; Wager, Stefan; Chouldechova, Alexandra; Tibshirani, Robert
• 署名单位: Carnegie Mellon University; Stanford University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12122
• 发表日期: 2016
• 页码: 423-444
• 关键词: stability selection; variable selection; drug-resistance; error control; regression; Lasso; MODEL

摘要

We consider a multiple-hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block H1,...,Hk of hypotheses. A rejection rule in this setting amounts to a procedure for choosing the stopping point k. This setting is inspired by the sequential nature of many model selection problems, where choosing a stopping point or a model is equivalent to rejecting all hypotheses up to that point and none thereafter. We propose two new testing procedures and prove that they control the false discovery rate in the ordered testing setting. We also show how the methods can be applied to model selection by using recent results on p-values in sequential model selection settings.