  Many classification techniques when the data are curves or functions have
been recently proposed. However, the presence of misaligned problems in the
curves can influence the performance of most of them. In this paper, we propose
a model-based approach for simultaneous curve registration and classification.
The method is proposed to perform curve classification based on a functional
logistic regression model that relies on both scalar variables and functional
variables, and to align curves simultaneously via a data registration model.
EM-based algorithms are developed to perform maximum likelihood inference of
the proposed models. We establish the identifiability results for curve
registration model and investigate the asymptotic properties of the proposed
estimation procedures. Simulation studies are conducted to demonstrate the
finite sample performance of the proposed models. An application of the hyoid
bone movement data from stroke patients reveals the effectiveness of the new
models.
