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

Bin Sun () and Yuehua Wu ()
Additional contact information
Bin Sun: York University, Department of Mathematics and Statistics
Yuehua Wu: York University, Department of Mathematics and Statistics

A chapter in Quantitative Methods in Environmental and Climate Research, 2018, pp 49-62 from Springer

Abstract: 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.

Keywords: Change-point detection; EM-type algorithm; General spatiotemporal autoregressive model; M-estimation; Outlier (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-01584-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030015848

DOI: 10.1007/978-3-030-01584-8_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().