Inference for Dispersion and Curvature of Random Objects

TL;DR

This paper develops a statistical framework for analyzing the dispersion and curvature of random objects in non-linear metric spaces, introducing a new test for intrinsic curvature based on asymptotic properties of dispersion measures.

Contribution

It derives a CLT for joint dispersion measures in geodesic spaces and proposes a novel curvature inference test applicable to various data types.

Findings

The CLT links Alexandrov curvature to dispersion measures.

The curvature test effectively detects intrinsic space curvature.

Finite-sample behavior is validated on real data examples.

Abstract

There are many open questions pertaining to the statistical analysis of random objects, which are increasingly encountered. A major challenge is the absence of linear operations in such spaces. A basic statistical task is to quantify statistical dispersion or spread. For two measures of dispersion for data objects in geodesic metric spaces, Fr\'echet variance and metric variance, we derive a central limit theorem (CLT) for their joint distribution. This analysis reveals that the Alexandrov curvature of the geodesic space determines the relationship between these two dispersion measures. This suggests a novel test for inferring the curvature of a space based on the asymptotic distribution of the dispersion measures. We demonstrate how this test can be employed to detect the intrinsic curvature of an unknown underlying space, which emerges as a joint property of the space and the underlying probability measure that generates the random objects. We investigate the asymptotic properties of the test and its finite-sample behavior for various data types, including distributional data and point cloud data. We illustrate the proposed inference for intrinsic curvature of random objects using gait synchronization data represented as symmetric positive definite matrices and energy compositional data on the sphere.