Optimal post-selection inference for sparse signals: a nonparametric empirical-Bayes approach

TL;DR

This paper introduces a nonparametric empirical-Bayes method for constructing optimal, selection-adjusted confidence sets for sparse signals, achieving shorter intervals with exact coverage and asymptotic optimality compared to existing approaches.

Contribution

It proposes a novel nonparametric empirical-Bayes approach that produces shorter, valid confidence sets for signals after selection, with proven asymptotic optimality and improved finite-sample performance.

Findings

Method produces confidence sets as short as possible on average.

Guarantees exact frequentist coverage uniformly over the parameter space.

Outperforms existing techniques in examples, with shorter confidence sets and maintained coverage.

Abstract

Many recently developed Bayesian methods have focused on sparse signal detection. However, much less work has been done addressing the natural follow-up question: how to make valid inferences for the magnitude of those signals after selection. Ordinary Bayesian credible intervals suffer from selection bias, owing to the fact that the target of inference is chosen adaptively. Existing Bayesian approaches for correcting this bias produce credible intervals with poor frequentist properties, while existing frequentist approaches require sacrificing the benefits of shrinkage typical in Bayesian methods, resulting in confidence intervals that are needlessly wide. We address this gap by proposing a nonparametric empirical-Bayes approach for constructing optimal selection-adjusted confidence sets. Our method produces confidence sets that are as short as possible on average, while both adjusting for selection and maintaining exact frequentist coverage uniformly over the parameter space. Our main theoretical result establishes an important consistency property of our procedure: that under mild conditions, it asymptotically converges to the results of an oracle-Bayes analysis in which the prior distribution of signal sizes is known exactly. Across a series of examples, the method outperforms existing frequentist techniques for post-selection inference, producing confidence sets that are notably shorter but with the same coverage guarantee.