Optimal Function-on-Scalar Regression over Complex Domains

TL;DR

This paper develops optimal estimation methods for function-on-scalar regression models over complex multi-dimensional and manifold domains, achieving minimax rates and demonstrating their effectiveness through numerical and real-world 3D facial imaging applications.

Contribution

It introduces a reproducing kernel Hilbert space-based estimator that attains minimax convergence rates for complex domains, extending RKHS-Sobolev links to Riemannian manifolds.

Findings

Estimator achieves minimax optimal rates.

Extension of RKHS-Sobolev link to manifolds.

Successful application to 3D facial imaging.

Abstract

In this work we consider the problem of estimating function-on-scalar regression models when the functions are observed over multi-dimensional or manifold domains and with potentially multivariate output. We establish the minimax rates of convergence and present an estimator based on reproducing kernel Hilbert spaces that achieves the minimax rate. To better interpret the derived rates, we extend well-known links between RKHS and Sobolev spaces to the case where the domain is a compact Riemannian manifold. This is accomplished using an interesting connection to Weyl's Law from partial differential equations. We conclude with a numerical study and an application to 3D facial imaging.