A Wavelet-Based Independence Test for Functional Data with an Application to MEG Functional Connectivity

TL;DR

This paper introduces a wavelet-based, model-free independence test for functional data using HSIC, with applications to MEG data, demonstrating improved interpretability and robustness over existing methods.

Contribution

It develops a novel wavelet-based pre-smoothing approach combined with HSIC for testing independence in functional data, addressing model misspecification issues.

Findings

Superior numerical performance in simulations

More interpretable functional connectivity patterns in MEG data

Asymptotic analysis confirms method validity

Abstract

Measuring and testing the dependency between multiple random functions is often an important task in functional data analysis. In the literature, a model-based method relies on a model which is subject to the risk of model misspecification, while a model-free method only provides a correlation measure which is inadequate to test independence. In this paper, we adopt the Hilbert-Schmidt Independence Criterion (HSIC) to measure the dependency between two random functions. We develop a two-step procedure by first pre-smoothing each function based on its discrete and noisy measurements and then applying the HSIC to recovered functions. To ensure the compatibility between the two steps such that the effect of the pre-smoothing error on the subsequent HSIC is asymptotically negligible, we propose to use wavelet soft-thresholding for pre-smoothing and Besov-norm-induced kernels for HSIC. We also provide the corresponding asymptotic analysis. The superior numerical performance of the proposed method over existing ones is demonstrated in a simulation study. Moreover, in an magnetoencephalography (MEG) data application, the functional connectivity patterns identified by the proposed method are more anatomically interpretable than those by existing methods.