Uniformly valid confidence intervals post-model-selection
Journal
Annals of Statistics
Type
journal article
Date Issued
2020
Author(s)
Bachoc, François
;
Preinerstorfer, David
;
Steinberger, Lukas
Abstract
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.
Language
English
Keywords
Inference post-model-selection
regression
uniform asymptotic inference
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Institute of Mathematical Statistics
Volume
48
Number
1
Start page
440
End page
463
Official URL
Subject(s)
Eprints ID
266209