Selective inference with a randomized response

TL;DR

This paper introduces a randomized response method for selective inference, enhancing test power and enabling consistent estimation, with theoretical guarantees and a framework for combining multiple procedures.

Contribution

It proposes a novel randomized response approach for selective inference, providing theoretical results and a framework for multiple procedure combination.

Findings

More powerful selective tests after randomized selection

Establishment of a selective central limit theorem

Framework for inference after multiple randomized procedures

Abstract

Inspired by sample splitting and the reusable holdout introduced in the field of differential privacy, we consider selective inference with a randomized response. We discuss two major advantages of using a randomized response for model selection. First, the selectively valid tests are more powerful after randomized selection. Second, it allows consistent estimation and weak convergence of selective inference procedures. Under independent sampling, we prove a selective (or privatized) central limit theorem that transfers procedures valid under asymptotic normality without selection to their corresponding selective counterparts. This allows selective inference in nonparametric settings. Finally, we propose a framework of inference after combining multiple randomized selection procedures. We focus on the classical asymptotic setting, leaving the interesting high-dimensional asymptotic questions for future work.