Wasserstein Regression

TL;DR

This paper introduces a novel Wasserstein metric-based regression model for univariate probability distributions, extending multivariate and functional regression to distributional data, with theoretical and empirical validation.

Contribution

It develops a new distribution-to-distribution regression framework using Wasserstein geometry, outperforming existing transformation-based methods.

Findings

Better predictive performance than log quantile density transformation approach.

Derived asymptotic convergence rates for estimators.

Extended framework to autoregressive models for distributional time series.

Abstract

The analysis of samples of random objects that do not lie in a vector space is gaining increasing attention in statistics. An important class of such object data is univariate probability measures defined on the real line. Adopting the Wasserstein metric, we develop a class of regression models for such data, where random distributions serve as predictors and the responses are either also distributions or scalars. To define this regression model, we utilize the geometry of tangent bundles of the space of random measures endowed with the Wasserstein metric for mapping distributions to tangent spaces. The proposed distribution-to-distribution regression model provides an extension of multivariate linear regression for Euclidean data and function-to-function regression for Hilbert space valued data in functional data analysis. In simulations, it performs better than an alternative transformation approach where one maps distributions to a Hilbert space through the log quantile density transformation and then applies traditional functional regression. We derive asymptotic rates of convergence for the estimator of the regression operator and for predicted distributions and also study an extension to autoregressive models for distribution-valued time series. The proposed methods are illustrated with data on human mortality and distributional time series of house prices.