A goodness-of-fit test for the functional linear model with functional response

TL;DR

This paper introduces a new goodness-of-fit test for the functional linear model with functional response, using a Cramér-von Mises norm and bootstrap calibration, with demonstrated effectiveness through simulations and real data.

Contribution

It proposes a novel, computationally efficient goodness-of-fit test for the FLMFR, including a new estimator and a bootstrap calibration method.

Findings

The test performs well in finite samples based on simulations.

The new estimator improves test reliability.

Application to real datasets demonstrates practical utility.

Abstract

The Functional Linear Model with Functional Response (FLMFR) is one of the most fundamental models to assess the relation between two functional random variables. In this paper, we propose a novel goodness-of-fit test for the FLMFR against a general, unspecified, alternative. The test statistic is formulated in terms of a Cram\'er-von Mises norm over a doubly-projected empirical process which, using geometrical arguments, yields an easy-to-compute weighted quadratic norm. A resampling procedure calibrates the test through a wild bootstrap on the residuals and the use of convenient computational procedures. As a sideways contribution, and since the statistic requires a reliable estimator of the FLMFR, we discuss and compare several regularized estimators, providing a new one specifically convenient for our test. The finite sample behavior of the test is illustrated via a simulation study. Also, the new proposal is compared with previous significance tests. Two novel real datasets illustrate the application of the new test.