EXACT POST-SELECTION INFERENCE, WITH APPLICATION TO THE LASSO

• 成果类型: Article
• 署名作者: Lee, Jason D.; Sun, Dennis L.; Sun, Yuekai; Taylor, Jonathan E.
• 署名单位: University of California System; University of California Berkeley; California State University System; California Polytechnic State University San Luis Obispo; University of California System; University of California Berkeley; Stanford University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/15-AOS1371
• 发表日期: 2016
• 页码: 907-927
• 关键词: false discovery rate; the-losers design; confidence-intervals; model-selection; parameters; estimators

摘要

We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes. the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.