Before revision: We propose a framework for ranking confidence interval estimators in terms of their uniform coverage accuracy. The key ingredient is the (existence and) quantification of the error in coverage of competing confidence intervals , uniformly over some empirically-relevant class of data generating processes.
After revision: In this paper we develop new confidence intervals for local polynomial regression, which minimize their worse-case coverage error and length in large samples. Our results rely on novel , uniformly over some empirically-relevant class of data generating processes.