More Efficient Exact Group-Invariance Testing: using a Representative Subgroup

TL;DR

This paper introduces a subgroup-based approach to group-invariance testing that enhances power and efficiency over traditional random sampling methods, especially in high-dimensional or large group scenarios.

Contribution

It proposes using a well-designed subgroup of transformations for invariance testing, ensuring exactness and improved power compared to random sampling.

Findings

Subgroup-invariance tests are more powerful than random sampling tests.

The method is effective in generalized location models.

Power improvements are comparable to Monte Carlo Z- and t-tests.

Abstract

Non-parametric tests based on permutation, rotation or sign-flipping are examples of group-invariance tests. These tests test invariance of the null distribution under a set of transformations that has a group structure, in the algebraic sense. Such groups are often huge, which makes it computationally infeasible to test using the entire group. Hence, it is standard practice to test using a randomly sampled set of transformations from the group. This random sample still needs to be substantial to obtain good power and replicability. We improve upon this standard practice by using a well-designed subgroup of transformations instead of a random sample. The resulting subgroup-invariance test is still exact, as invariance under a group implies invariance under its subgroups. We illustrate this in a generalized location model and obtain more powerful tests based on the same number of transformations. In particular, we show that a subgroup-invariance test is consistent for lower signal-to-noise ratios than a test based on a random sample. For the special case of a normal location model and a particular design of the subgroup, we show that the power improvement is equivalent to the power difference between a Monte Carlo $Z$-test and a Monte Carlo $t$-test.