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A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals

  • Olivier Bock
  • Xavier Collilieux
  • François Guillamon
  • Emilie LebarbierEmail author
  • Claire Pascal
Article

Abstract

This work is motivated by an application for the homogenization of global navigation satellite system (GNSS)-derived integrated water vapour series. Indeed, these series are affected by abrupt changes due to equipment changes or environmental effects. The detection and correction of the series from these changes are a crucial step before any use for climate studies. In addition to these abrupt changes, it has been observed in the series a non-stationary of the variability. We propose in this paper a new segmentation model that is a breakpoint detection in the mean model of a Gaussian process with heterogeneous variance on known time intervals. In this segmentation case, the dynamic programming algorithm used classically to infer the breakpoints cannot be applied anymore. We propose a procedure in two steps: we first estimate robustly the variances and then apply the classical inference by plugging these estimators. The performance of our proposed procedure is assessed through simulation experiments. An application to real GNSS data is presented.

Keywords

Segmentation Robust estimator of the variance parameter GNSS time series 

Notes

References

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Olivier Bock
    • 1
  • Xavier Collilieux
    • 1
    • 2
  • François Guillamon
    • 1
    • 3
    • 4
  • Emilie Lebarbier
    • 3
    • 4
    Email author
  • Claire Pascal
    • 1
    • 3
    • 4
  1. 1.IGN LAREG, Univ Paris DiderotParis Cedex 13France
  2. 2.Observatoire de Paris, SYRTE, CNRS, UPMCParisFrance
  3. 3.AgroParisTech UMR518Paris 5eFrance
  4. 4.INRA UMR518Paris 5eFrance

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