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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Altieri L, Cocchi D, Greco F, Ellian JB, Scott EM (2016) Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes. J Stat Comput Simul 86:2531–2545
Huber PJ (1973) Robust regression. Ann Stat 1:799–821
Killick R, Eckley I (2014) Changepoint: an R package for changepoint analysis. J Stat Softw 58:1–13
Nappi-Choulet I, Maury T-P (2009) A spatiotemporal autoregressive price index for the Paris office property market. Real Estate Econ V37:305–340
Otto P, Schmid W (2016) Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models. Biometrical J 58:1113–1137
Porter PS, Rao ST, Zurbenko IG, Dunker AM, Wolff GT (2001) Ozone air quality over North America: part II-an analysis of trend detection and attribution techniques. J Air Waste Manag Assoc 51:283–306
Wu Y, Jin B, Chan E (2015) Detection of Changes in Ground-level ozone concentrations via entropy. Entropy 17:2749–2763
Wu Y, Sun X, Chan E, Qin S (2017) Detecting non-negligible new influences in environmental data via a general spatio-temporal autoregressive Model. Br J Environ Clim Chang 7(4):223–235
Wyse J, Friel N, Rue H (2011) Approximate simulation-free Bayesian inference for multiple changepoint models with dependence within segments. Bayesian Anal 6:501–528
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
A specific example is given to show how to calculate \(y_{T_{1}(k-1)+t}^{(1)}\), \(y_{T_{1}(k-1)+t}^{(2)}\), \(\tilde {y}_{T_{1}(k-1)+t}^{(3)}\) and \(\tilde {\epsilon }_{T_{1}(k-1)+t}\) in Sect. 2.1.
Consider t 1 = 1, \(\mathcal {S}_{1}=\{1,2,\ldots ,90,307,308,\ldots ,366\}\) for Winter, \(\mathcal {S}_{2}=\{91,92,\ldots ,152\}\) for Spring, \(\mathcal {S}_{3}=\{153,\ldots ,244\}\) for Summer and \(\mathcal {S}_{4}=\{245,\ldots ,306\}\) for Fall. In this case, T = 366 and T 1 = 181. For winter,
For Spring,
For Summer,
For Fall,
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, B., Wu, Y. (2018). Detection of Change Points in Spatiotemporal Data in the Presence of Outliers and Heavy-Tailed Observations. In: Cameletti, M., Finazzi, F. (eds) Quantitative Methods in Environmental and Climate Research. Springer, Cham. https://doi.org/10.1007/978-3-030-01584-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-01584-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01583-1
Online ISBN: 978-3-030-01584-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)