Wasserstein F-Tests for Frechet Regression on Bures-Wasserstein Manifolds

Abstract

This paper addresses regression analysis for covariance matrix-valued outcomes with Euclidean covariates, motivated by applications in single-cell genomics and neuroscience where covariance matrices are observed across many samples. Our analysis leverages Fr\'echet regression on the Bures-Wasserstein manifold to estimate the conditional Fr\'echet mean given covariates $x$. We establish a non-asymptotic uniform $\sqrt{n}$-rate of convergence (up to logarithmic factors) over covariates with $\|x\| \lesssim \sqrt{\log n}$ and derive a pointwise central limit theorem to enable statistical inference. For testing covariate effects, we devise a novel test whose null distribution converges to a weighted sum of independent chi-square distributions, with power guarantees against a sequence of contiguous alternatives. Simulations validate the accuracy of the asymptotic theory. Finally, we apply our methods to a single-cell gene expression dataset, revealing age-related changes in gene co-expression networks.