Splitting strategies for post-selection inference

TL;DR

This paper introduces a randomized response approach for post-selection inference in sparse regression, offering higher power and validity compared to traditional data splitting methods.

Contribution

It proposes a novel randomization-based method for post-selection inference that improves power and flexibility over existing techniques.

Findings

Randomization approach outperforms data splitting in power.

Theoretical CLT established for the randomization method.

Empirical results confirm increased inference accuracy.

Abstract

We consider the problem of providing valid inference for a selected parameter in a sparse regression setting. It is well known that classical regression tools can be unreliable in this context due to the bias generated in the selection step. Many approaches have been proposed in recent years to ensure inferential validity. Here, we consider a simple alternative to data splitting based on randomising the response vector, which allows for higher selection and inferential power than the former and is applicable with an arbitrary selection rule. We provide a theoretical and empirical comparison of both methods and derive a Central Limit Theorem for the randomisation approach. Our investigations show that the gain in power can be substantial.