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

  • Bin Sun
  • Yuehua WuEmail author
Conference paper


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.


Change-point detection EM-type algorithm General spatiotemporal autoregressive model M-estimation Outlier 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsYork UniversityTorontoCanada

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