Unified Operator Framework for Functional and Multivariate Regression

TL;DR

This paper introduces a unified operator framework for various regression models, clarifying their relationships and the impact of discretization, with theoretical insights and simulation validation.

Contribution

It presents a comprehensive operator-based approach that unifies scalar, multivariate, and functional regression, linking discrete and continuous representations.

Findings

Discrete representations converge to continuous operators as observation grids refine.

Estimation under discrete measures reduces to classical multivariate regression.

The framework explains why vectorized multivariate regression often performs well in linear settings.

Abstract

We develop a unified operator framework for scalar, multivariate, and functional regression based on integral operators defined with respect to general measures. Within this framework, classical regression models, including scalar-on-function, function-on-scalar, function-on-function, and multivariate multiple regression, arise as special cases corresponding to different choices of input and output measures. We establish three main results. First, we show that the standard regression taxonomy can be expressed as a single operator under varying measures. Second, we demonstrate that discrete representations correspond to exact operator evaluations under discrete measures and converge to the continuous operator as the observation grid is refined. Third, we show that estimation under the discrete-measure formulation reduces to standard multivariate regression, with statistical properties governed by classical results. A simulation study illustrates these principles, highlighting the roles of discretization, conditioning, and estimation. Overall, the proposed framework clarifies the relationship between functional and multivariate regression and provides a meaningful interpretation of discretized modeling approaches as operator estimation under different measure specifications. This perspective also explains why vectorized multivariate regression is often competitive with functional methods in linear settings: it directly estimates the discrete-measure representation of the underlying operator.