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In this paper, we argue that the problem of registering two sets offunctional data, where the underlying mean function has sharp features, is notproperly addressed by methods designed to align a bunch of growth curves data.We provide a new method, which is able to pool local information withoutsmoothing and to match sharp landmarks without manual identification. Thismethod, which we refer to as kernel-matched registration, is based onmaximizing a kernel-based measure of alignment. We prove that the proposedmethod is consistent under fairly general conditions. Simulation results showsuperiority of the performance of the proposed method over two existingmethods. The proposed method is illustrated through the analysis of three setsof paleoclimatic data.

Feature Sensitive Curve Registration by Kernel Matching