Inference for Fr\'echet Regression

TL;DR

This paper develops statistical inference methods, including significance and partial effect tests, for Fréchet regression models where responses are non-Euclidean objects in metric spaces, addressing a gap in the literature.

Contribution

It introduces significance testing procedures for Fréchet regression, including null hypothesis testing and partial effect testing, using novel multiplier and Cauchy combination techniques.

Findings

Proposed tests have good finite sample performance in simulations.

Methods are applicable to network and spherical data, demonstrated through real data examples.

Abstract

Linear regression is widely used to model relationships between responses and predictors. In modern applications, one encounters data where the responses are non-Euclidean random objects situated in a metric space, paired with Euclidean predictors. Global Fr\'echet regression generalizes linear regression to such general settings, however statistical inference has remained largely unexplored. We develop a significance test for the null hypothesis that the Fr\'echet regression function does not depend on the predictors, addressing the challenge of an absence of linear operations in metric spaces. We also develop a test for the partial effect of a subset of the predictors in analogy to, but quite different from, the partial F-tests commonly used in classical linear regression under Gaussian assumptions. Key ideas are to employ random multipliers to obtain non-degenerate null distributions for the proposed test statistics and the Cauchy combination method. We obtain consistency and convergence results under the null hypothesis and contiguous alternatives and demonstrate the finite sample performance of the proposed tests through simulations on network data represented by graph Laplacians and spherical data with geodesic distances. We further illustrate our method using transport networks arising from New York City taxi trip data and U.S. energy source compositional data.