Adaptive Global Testing for Functional Linear Models

Abstract

This article studies global testing of the slope function in functional linear regression models. A major challenge in functional global testing is to choose the dimension of projection when approximating the functional regression model by a finite dimensional multivariate linear regression model. We develop a new method that simultaneously tests the slope vectors in a sequence of functional principal components regression models. The sequence of models being tested is determined by the sample size and is an integral part of the testing procedure. Our theoretical analysis shows that the proposed method is uniformly powerful over a class of smooth alternatives when the signal to noise ratio exceeds the detection boundary. The methods and results reflect the deep connection between the functional linear regression model and the Gaussian sequence model. We also present an extensive simulation study and a real data example to illustrate the finite sample performance of our method. Supplementary materials for this article are available online.

KEY WORDS:

• Adaptive testing
• Detection boundary
• Functional linear regression
• Functional principal components