Estimation of functional derivatives

TL;DR

This paper introduces a kernel-based method for estimating functional derivatives in models with functional predictors and scalar responses, providing a way to understand the relationship between predictor changes and responses.

Contribution

It proposes a novel nonparametric approach using eigenfunction decomposition to estimate functional derivatives, extending classical gradient concepts to functional data.

Findings

The method achieves asymptotic consistency in estimating derivatives.

Application to growth curves demonstrates practical utility.

Eigenfunction decomposition effectively captures directions of steepest descent.

Abstract

Situations of a functional predictor paired with a scalar response are increasingly encountered in data analysis. Predictors are often appropriately modeled as square integrable smooth random functions. Imposing minimal assumptions on the nature of the functional relationship, we aim to estimate the directional derivatives and gradients of the response with respect to the predictor functions. In statistical applications and data analysis, functional derivatives provide a quantitative measure of the often intricate relationship between changes in predictor trajectories and those in scalar responses. This approach provides a natural extension of classical gradient fields in vector space and provides directions of steepest descent. We suggest a kernel-based method for the nonparametric estimation of functional derivatives that utilizes the decomposition of the random predictor functions into their eigenfunctions. These eigenfunctions define a canonical set of directions into which the gradient field is expanded. The proposed method is shown to lead to asymptotically consistent estimates of functional derivatives and is illustrated in an application to growth curves.