Skip to main content
Article thumbnail
Location of Repository

Alignment of Curves by Non-Parametric Maximum Likelihood Estimation

By Birgitte B. Rønn


Alignment of curves by non-parametric maximum likelihood estimation can be done when the individual transformations of the time axis is modelled by unobserved random shifts. We consider the larger class of models, where the individual time transformations are assumed to be of a parametric form, known up to some individual un-observed random parameters. The nonparametric maximum likelihood approach is used to derive a infinite dimensional score equation, when differentiating with respect to the common shape function. We suggest an algorithm to find the non-parametric maximum likelihood estimator (NPMLE) for the shape function and apply the method to two data examples on feta cheese and crop respectively. We find smooth estimates for the shape functions without choosing any smoothing parameters or kernel function and we estimate realisations of the un-observed transformation parameters that align the curves to satisfy the eye.

Year: 2007
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.