A partial least squares approach for function-on-function interaction regression

TL;DR

This paper introduces a partial least squares regression method for estimating complex function-on-function models with interaction effects, effectively handling the infinite-dimensional nature of functional data through basis expansion and model selection.

Contribution

It develops a novel PLS approach for function-on-function regression with interactions, providing explicit coefficient estimates and a forward model selection procedure.

Findings

Method performs well in Monte Carlo simulations.

Outperforms existing methods in empirical data analyses.

Provides explicit formulas for coefficient estimation.

Abstract

A partial least squares regression is proposed for estimating the function-on-function regression model where a functional response and multiple functional predictors consist of random curves with quadratic and interaction effects. The direct estimation of a function-on-function regression model is usually an ill-posed problem. To overcome this difficulty, in practice, the functional data that belong to the infinite-dimensional space are generally projected into a finite-dimensional space of basis functions. The function-on-function regression model is converted to a multivariate regression model of the basis expansion coefficients. In the estimation phase of the proposed method, the functional variables are approximated by a finite-dimensional basis function expansion method. We show that the partial least squares regression constructed via a functional response, multiple functional predictors, and quadratic/interaction terms of the functional predictors is equivalent to the partial least squares regression constructed using basis expansions of functional variables. From the partial least squares regression of the basis expansions of functional variables, we provide an explicit formula for the partial least squares estimate of the coefficient function of the function-on-function regression model. Because the true forms of the models are generally unspecified, we propose a forward procedure for model selection. The finite sample performance of the proposed method is examined using several Monte Carlo experiments and two empirical data analyses, and the results were found to compare favorably with an existing method.