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

• 成果类型: Article
• 署名作者: Kivaranovic, Danijel; Leeb, Hannes
• 署名单位: University of Vienna
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1732989
• 发表日期: 2021
• 页码: 845-857
• 关键词: estimators; shrinkage; inference

摘要

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 kappa-quantiles behave like for kappa close to 1. Our results can also be used to analyze other confidence intervals that are based on the polyhedral method.