Dynamic Retrospective Regression for Functional Data

Existing functional regression methods do not handle phase variability in an efficient way. In this article we propose a functional regression method that incorporates phase synchronization as an intrinsic part of the model, and then attains better predictive power than ordinary linear regression in a simple and parsimonious way.

The finite-sample properties of the estimators are studied by simulation. As an example of application, we analyze neuromotor data arising from a study of human lip movement. This article has supplementary materials online.