Functional linear regression via canonical analysis

TL;DR

This paper introduces a novel approach to functional linear regression using canonical analysis, providing new theoretical insights, estimation methods, and an application to mortality data in aging studies.

Contribution

It develops a representation of the regression parameter via canonical components, linking functional regression with canonical analysis, and proposes an alternative estimation procedure.

Findings

Representation of regression parameter in terms of canonical components

Comparison of canonical expansion method with principal component regression

Application to mortality data in aging research

Abstract

We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.