  In this paper, we argue that the problem of registering two sets of
functional data, where the underlying mean function has sharp features, is not
properly addressed by methods designed to align a bunch of growth curves data.
We provide a new method, which is able to pool local information without
smoothing and to match sharp landmarks without manual identification. This
method, which we refer to as kernel-matched registration, is based on
maximizing a kernel-based measure of alignment. We prove that the proposed
method is consistent under fairly general conditions. Simulation results show
superiority of the performance of the proposed method over two existing
methods. The proposed method is illustrated through the analysis of three sets
of paleoclimatic data.
