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Pairwise curve synchronization for functional data

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  • Rong Tang
  • Hans-Georg Müller
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    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. Copyright 2008, Oxford University Press.

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    File URL: http://hdl.handle.net/10.1093/biomet/asn047
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    Bibliographic Info

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 95 (2008)
    Issue (Month): 4 ()
    Pages: 875-889

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    Handle: RePEc:oup:biomet:v:95:y:2008:i:4:p:875-889

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    Cited by:
    1. Boudaoud, S. & Rix, H. & Meste, O., 2010. "Core Shape modelling of a set of curves," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 308-325, February.
    2. Dimeglio, Chloé & Gallón, Santiago & Loubes, Jean-Michel & Maza, Elie, 2014. "A robust algorithm for template curve estimation based on manifold embedding," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 373-386.
    3. Irene Albarrán Lozano & Pablo Alonso González & Ana Arribas Gil, 2013. "Dependency evolution in Spanish disabled population : a functional data analysis approach," Statistics and Econometrics Working Papers ws130403, Universidad Carlos III, Departamento de Estadística y Econometría.
    4. Arribas-Gil, Ana & Müller, Hans-Georg, 2014. "Pairwise dynamic time warping for event data," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 255-268.

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