Single and multiple index functional regression models with nonparametric link

TL;DR

This paper introduces a novel nonparametric approach for functional regression that estimates link functions and multiple indices adaptively, achieving polynomial convergence rates and demonstrating promising finite sample performance.

Contribution

It proposes a new nonparametric method for estimating link functions and multi-index models in functional regression, improving flexibility and convergence rates.

Findings

Achieves polynomial convergence rates in prediction.

Demonstrates good finite sample performance in simulations.

Successfully applied to a real functional regression problem.

Abstract

Fully nonparametric methods for regression from functional data have poor accuracy from a statistical viewpoint, reflecting the fact that their convergence rates are slower than nonparametric rates for the estimation of high-dimensional functions. This difficulty has led to an emphasis on the so-called functional linear model, which is much more flexible than common linear models in finite dimension, but nevertheless imposes structural constraints on the relationship between predictors and responses. Recent advances have extended the linear approach by using it in conjunction with link functions, and by considering multiple indices, but the flexibility of this technique is still limited. For example, the link may be modeled parametrically or on a grid only, or may be constrained by an assumption such as monotonicity; multiple indices have been modeled by making finite-dimensional assumptions. In this paper we introduce a new technique for estimating the link function nonparametrically, and we suggest an approach to multi-index modeling using adaptively defined linear projections of functional data. We show that our methods enable prediction with polynomial convergence rates. The finite sample performance of our methods is studied in simulations, and is illustrated by an application to a functional regression problem.