An RKHS approach for pivotal inference in functional linear regression

TL;DR

This paper introduces a novel RKHS-based method for hypothesis testing in functional linear regression, focusing on approximate nullity of the slope function in time series data without estimating nuisance parameters.

Contribution

It develops an asymptotically pivotal test for approximate nullity of the slope function in functional linear regression using RKHS, applicable to scalar-on-function and function-on-function models.

Findings

Proposes a test that does not require nuisance parameter estimation.

Validates the approach with simulations and real data.

Addresses approximate null hypotheses in functional regression.

Abstract

We develop methodology for testing hypotheses regarding the slope function in functional linear regression for time series via a reproducing kernel Hilbert space approach. In contrast to most of the literature, which considers tests for the exact nullity of the slope function, we are interested in the null hypothesis that the slope function vanishes only approximately, where deviations are measured with respect to the $L^2$-norm. An asymptotically pivotal test is proposed, which does not require the estimation of nuisance parameters and long-run covariances. The key technical tools to prove the validity of our approach include a uniform Bahadur representation and a weak invariance principle for a sequential process of estimates of the slope function. Both scalar-on-function and function-on-function linear regression are considered and finite-sample methods for implementing our methodology are provided. We also illustrate the potential of our methods by means of a small simulation study and a data example.