  Functional data often exhibit both amplitude and phase variation around a
common base shape, with phase variation represented by a so called warping
function. The process removing phase variation by curve alignment and inference
of the warping functions is referred to as curve registration. When functional
data are observed with substantial noise, model-based methods can be employed
for simultaneous smoothing and curve registration. However, the nonlinearity of
the model often renders the inference computationally challenging. In this
paper, we propose an alternative method for model-based curve registration
which is computationally more stable and efficient than existing approaches in
the literature. We apply our method to the analysis of elephant seal dive
profiles and show that more intuitive groupings can be obtained by clustering
on phase variations via the predicted warping functions.
