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Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations

In: Quantitative Methods in Environmental and Climate Research

Author

Listed:
  • Bin Sun

    (York University, Department of Mathematics and Statistics)

  • Yuehua Wu

    (York University, Department of Mathematics and Statistics)

Abstract

This work improves the estimation algorithm of a general spatiotemporal autoregressive model proposed by Wu et al. (Br J Environ Clim Chang 7(4):223–235, 2017). We substitute their least squares technique in the EM-type algorithm by M-estimation and also present an M-estimation based change-point detection procedure. In addition, data examples are provided.

Suggested Citation

  • Bin Sun & Yuehua Wu, 2018. "Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations," Springer Books, in: Michela Cameletti & Francesco Finazzi (ed.), Quantitative Methods in Environmental and Climate Research, pages 49-62, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-01584-8_3
    DOI: 10.1007/978-3-030-01584-8_3
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