A Small-Uniform Statistic for the Inference of Functional Linear Regressions

TL;DR

This paper introduces a new small-uniform statistic for functional linear regression that balances between pointwise and norm convergence, enabling improved hypothesis testing with better power properties.

Contribution

It proposes a novel small-uniform statistic based on fractional programming for functional PCA estimators, bridging the gap between existing asymptotic behaviors.

Findings

The statistic converges to the supremum of a Gaussian process.

Simulation results show improved hypothesis testing power.

The method is applicable with growing subspace dimensions.

Abstract

We propose a "small-uniform" statistic for the inference of the functional PCA estimator in a functional linear regression model. The literature has shown two extreme behaviors: on the one hand, the FPCA estimator does not converge in distribution in its norm topology; but on the other hand, the FPCA estimator does have a pointwise asymptotic normal distribution. Our statistic takes a middle ground between these two extremes: after a suitable rate normalization, our small-uniform statistic is constructed as the maximizer of a fractional programming problem of the FPCA estimator over a finite-dimensional subspace, and whose dimensions will grow with sample size. We show the rate for which our scalar statistic converges in probability to the supremum of a Gaussian process. The small-uniform statistic has applications in hypothesis testing. Simulations show our statistic has comparable to slightly better power properties for hypothesis testing than the two statistics of Cardot, Ferraty, Mas and Sarda (2003).