UNIFORMLY VALID CONFIDENCE INTERVALS POST-MODEL-SELECTION

• 成果类型: Article
• 署名作者: Bachoc, Francois; Preinerstorfer, David; Steinberger, Lukas
• 署名单位: Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Universite Libre de Bruxelles; University of Freiburg
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/19-AOS1815
• 发表日期: 2020
• 页码: 440-463
• 关键词: maximum-likelihood-estimation; inference; asymptotics; estimators; regression

摘要

We suggest general methods to construct asymptotically uniformly valid confidence intervals post-model-selection. The constructions are based on principles recently proposed by Berk et al. (Ann. Statist. 41 (2013) 802-837). In particular, the candidate models used can be misspecified, the target of inference is model-specific, and coverage is guaranteed for any data-driven model selection procedure. After developing a general theory, we apply our methods to practically important situations where the candidate set of models, from which a working model is selected, consists of fixed design homoskedastic or heteroskedastic linear models, or of binary regression models with general link functions. In an extensive simulation study, we find that the proposed confidence intervals perform remarkably well, even when compared to existing methods that are tailored only for specific model selection procedures.