Bootstrap inference in functional linear regression models with scalar response under heteroscedasticity

TL;DR

This paper develops a bootstrap inference method for functional linear regression models with scalar responses under heteroscedastic errors, establishing a central limit theorem and improving sampling distribution approximations.

Contribution

It introduces a debiased paired bootstrap method tailored for functional regressors, enhancing inference accuracy under heteroscedasticity.

Findings

The central limit theorem is established for the estimated conditional mean.

The proposed bootstrap method outperforms naive approaches in simulations.

Confidence intervals and hypothesis tests are effectively constructed using the new bootstrap.

Abstract

Inference for functional linear models in the presence of heteroscedastic errors has received insufficient attention given its practical importance; in fact, even a central limit theorem has not been studied in this case. At issue, conditional mean estimates have complicated sampling distributions due to the infinite dimensional regressors, where truncation bias and scaling issues are compounded by non-constant variance under heteroscedasticity. As a foundation for distributional inference, we establish a central limit theorem for the estimated conditional mean under general dependent errors, and subsequently we develop a paired bootstrap method to provide better approximations of sampling distributions. The proposed paired bootstrap does not follow the standard bootstrap algorithm for finite dimensional regressors, as this version fails outside of a narrow window for implementation with functional regressors. The reason owes to a bias with functional regressors in a naive bootstrap construction. Our bootstrap proposal incorporates debiasing and thereby attains much broader validity and flexibility with truncation parameters for inference under heteroscedasticity; even when the naive approach may be valid, the proposed bootstrap method performs better numerically. The bootstrap is applied to construct confidence intervals for centered projections and for conducting hypothesis tests for the multiple conditional means. Our theoretical results on bootstrap consistency are demonstrated through simulation studies and also illustrated with a real data example.