Skip Navigation


Biometrika Advance Access originally published online on November 5, 2008
Biometrika 2008 95(4):875-889; doi:10.1093/biomet/asn047
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
95/4/875    most recent
asn047v1
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Tang, R.
Right arrow Articles by Müller, H.-G.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2008 Biometrika Trust

Articles

Pairwise curve synchronization for functional data

Rong Tang

Division of Biostatistics, Center for Devices and Radiological Health, Food and Drug Administration, Rockville, Maryland 20850, U.S.A. rong.tang{at}fda.hhs.gov

Hans-Georg Müller

Department of Statistics, University of California, Davis, California 95616, U.S.A. mueller{at}wald.ucdavis.edu

Received for publication 1 May 2007. Revision received 1 April 2008.
  Abstract

Data collected by scientists are increasingly in the form of trajectories or curves. Often these can be viewed as realizations of a composite process driven by both amplitude and time variation. We consider the situation in which functional variation is dominated by time variation, and develop a curve-synchronization method that uses every trajectory in the sample as a reference to obtain pairwise warping functions in the first step. These initial pairwise warping functions are then used to create improved estimators of the underlying individual warping functions in the second step. A truncated averaging process is used to obtain robust estimation of individual warping functions. The method compares well with other available time-synchronization approaches and is illustrated with Berkeley growth data and gene expression data for multiple sclerosis.

Key Words: Alignment • Curve registration • Functional data analysis • Gene expression profile • Multiple sclerosis • Synchronization • Time warping


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.