On the Length of Post-Model-Selection Confidence Intervals Conditional on Polyhedral Constraints

Author(s)

Danijel Kivaranovic, Hannes Leeb

Abstract

Valid inference after model selection is currently a very active area of research. The polyhedral method, introduced in an article by Lee et al., allows for valid inference after model selection if the model selection event can be described by polyhedral constraints. In that reference, the method is exemplified by constructing two valid confidence intervals when the Lasso estimator is used to select a model. We here study the length of these intervals. For one of these confidence intervals, which is easier to compute, we find that its expected length is always infinite. For the other of these confidence intervals, whose computation is more demanding, we give a necessary and sufficient condition for its expected length to be infinite. In simulations, we find that this sufficient condition is typically satisfied, unless the selected model includes almost all or almost none of the available regressors. For the distribution of confidence interval length, we find that the κ-quantiles behave like 1/(1−κ) for κ close to 1. Our results can also be used to analyze other confidence intervals that are based on the polyhedral method.

Organisation(s)

Department of Statistics and Operations Research, Research Network Data Science

Journal

American Statistical Association. Journal

Volume

116

Pages

845-857

No. of pages

13

ISSN

0162-1459

DOI

https://doi.org/10.1080/01621459.2020.1732989

Publication date

05-2020

Peer reviewed

Yes

Austrian Fields of Science 2012

101029 Mathematical statistics

Keywords

ASJC Scopus subject areas

Statistics and Probability, Statistics, Probability and Uncertainty

Portal url

https://ucris.univie.ac.at/portal/en/publications/on-the-length-of-postmodelselection-confidence-intervals-conditional-on-polyhedral-constraints(cc0e1d66-d169-4c99-be56-9efeeb3c0868).html