Post-selection point and interval estimation of signal sizes in Gaussian samples

TL;DR

This paper introduces a novel post-selection inference method for estimating signal sizes in Gaussian samples, leveraging recent advances in Lasso-based inference, and demonstrates its effectiveness and theoretical optimality in sparse settings.

Contribution

It adapts recent post-selection inference techniques to the orthogonal setting for estimating multiple means, providing new point and interval estimates with proven risk bounds.

Findings

Proposed estimators perform well against existing methods.

Established an upper bound on worst-case risk within a constant factor of minimax risk.

Framework applies to various selection procedures like top-K and BH.

Abstract

We tackle the problem of the estimation of a vector of means from a single vector-valued observation $y$. Whereas previous work reduces the size of the estimates for the largest (absolute) sample elements via shrinkage (like James-Stein) or biases estimated via empirical Bayes methodology, we take a novel approach. We adapt recent developments by Lee et al (2013) in post selection inference for the Lasso to the orthogonal setting, where sample elements have different underlying signal sizes. This is exactly the setup encountered when estimating many means. It is shown that other selection procedures, like selecting the $K$ largest (absolute) sample elements and the Benjamini-Hochberg procedure, can be cast into their framework, allowing us to leverage their results. Point and interval estimates for signal sizes are proposed. These seem to perform quite well against competitors, both recent and more tenured. Furthermore, we prove an upper bound to the worst case risk of our estimator, when combined with the Benjamini-Hochberg procedure, and show that it is within a constant multiple of the minimax risk over a rich set of parameter spaces meant to evoke sparsity.