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

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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.

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Authors and Affiliations

  1. IGN LAREG, Univ Paris Diderot, Sorbonne Paris Cit, 5 rue Thomas Mann, 75205, Paris Cedex 13, France

    Olivier Bock, Xavier Collilieux, François Guillamon & Claire Pascal

  2. Observatoire de Paris, SYRTE, CNRS, UPMC, 75014, Paris, France

    Xavier Collilieux

  3. AgroParisTech UMR518, Paris 5e, France

    François Guillamon, Emilie Lebarbier & Claire Pascal

  4. INRA UMR518, Paris 5e, France

    François Guillamon, Emilie Lebarbier & Claire Pascal

Authors
  1. Olivier Bock
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  2. Xavier Collilieux
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  3. François Guillamon
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  4. Emilie Lebarbier
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  5. Claire Pascal
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Correspondence to Emilie Lebarbier.

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Bock, O., Collilieux, X., Guillamon, F. et al. A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals. Stat Comput 30, 195–207 (2020). https://doi.org/10.1007/s11222-019-09853-5

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  • DOI: https://doi.org/10.1007/s11222-019-09853-5

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