Homogeneity test for functional data

TL;DR

This paper introduces new two-sample homogeneity tests for functional data analysis using depth measures, validated through simulations and real data applications, demonstrating their effectiveness in detecting differences.

Contribution

The paper develops novel homogeneity tests based on depth measures for functional data, providing a new approach with demonstrated efficiency and practical applicability.

Findings

Effective detection of shape and magnitude differences

Good performance on real data samples

New tests outperform some existing methods

Abstract

In the context of functional data analysis, we propose new two sample tests for homogeneity. Based on some well-known depth measures, we construct four different statistics in order to measure distance between the two samples. A simulation study is performed to check the efficiency of the tests when confronted with shape and magnitude perturbation. Finally, we apply these tools to measure the homogeneity in some samples of real data, obtaining good results using this new method.