Two New Tests for Equality of Several Covariance Functions

TL;DR

This paper introduces two novel statistical tests, the quasi GPF and quasi Fmax, for assessing the equality of covariance functions across multiple functional populations, with proven asymptotic properties and superior power in simulations.

Contribution

The paper develops two new tests for covariance function equality, deriving their asymptotic distributions and demonstrating improved power over existing methods through simulations.

Findings

The tests have asymptotic chi-squared-type null distributions.

Permutation methods effectively approximate null distributions for various sample sizes.

New tests outperform existing ones in simulation studies when covariance functions differ in scale.

Abstract

In this paper, we propose two new tests for testing the equality of the covariance functions of several functional populations, namely a quasi GPF test and a quasi $F_{\max}$ test. The asymptotic random expressions of the two tests under the null hypothesis are derived. We show that the asymptotic null distribution of the quasi GPF test is a chi-squared-type mixture whose distribution can be well approximated by a simple scaled chi-squared distribution. We also adopt a random permutation method for approximating the null distributions of the quasi GPF and $F_{\max}$ tests. The random permutation method is applicable for both large and finite sample sizes. The asymptotic distributions of the two tests under a local alternative are investigated and they are shown to be root-n consistent. Simulation studies are presented to demonstrate the finite-sample performance of the new tests against three existing tests. They show that our new tests are more powerful than the three existing tests when the covariance functions at different time points have different scales. An illustrative example is also presented.