  Samples of curves, or functional data, usually present phase variability in
addition to amplitude variability. Existing functional regression methods do
not handle phase variability in an efficient way. In this paper 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.
