Association and Independence Test for Random Objects

TL;DR

This paper introduces a comprehensive framework for testing independence and measuring association between complex random objects in various metric spaces, with theoretical guarantees and practical applications.

Contribution

It proposes the profile association measure, connects it with Hoeffding D statistic, and develops a permutation test applicable to diverse data types including networks and manifolds.

Findings

Profile independence test outperforms existing methods in simulations.

Framework applicable to high-dimensional, functional, and network data.

Demonstrated utility in brain connectivity and mortality data analysis.

Abstract

We develop a unified framework for testing independence and quantifying association between random objects that are located in general metric spaces. Special cases include functional and high-dimensional data as well as networks, covariance matrices and data on Riemannian manifolds, among other metric space-valued data. A key concept is the profile association, a measure based on distance profiles that intrinsically characterize the distributions of random objects in metric spaces. We rigorously establish a connection between the Hoeffding D statistic and the profile association and derive a permutation test with theoretical guarantees for consistency and power under alternatives to the null hypothesis of independence/no association. We extend this framework to the conditional setting, where the independence between random objects given a Euclidean predictor is of interest. In simulations across various metric spaces, the proposed profile independence test is found to outperform existing approaches. The practical utility of this framework is demonstrated with applications to brain connectivity networks derived from magnetic resonance imaging and age-at-death distributions for males and females obtained from human mortality data.