1,666
Views
5
CrossRef citations to date
0
Altmetric
General

Hierarchical Spatio-Temporal Change-Point Detection

ORCID Icon, ORCID Icon, &
Pages 390-400 | Received 19 Sep 2022, Accepted 09 Mar 2023, Published online: 11 Apr 2023

References

  • Aminikhanghahi, S., and Cook, D. J. (2017), “A Survey of Methods for Time Series Change Point Detection,” Knowledge and Information Systems, 51, 339–367. DOI: 10.1007/s10115-016-0987-z.
  • Aston, J. A., and Kirch, C. (2012), “Detecting and Estimating Changes in Dependent Functional Data,” Journal of Multivariate Analysis, 109, 204–220. DOI: 10.1016/j.jmva.2012.03.006.
  • Baddeley, A., Rubak, E., and Turner, R. (2015), Spatial Point Patterns: Methodology and Applications with R, Boca Raton, FL: CRC Press.
  • Berkes, I., Gabrys, R., Horváth, L., and Kokoszka, P. (2009), “Detecting Changes in the Mean of Functional Observations,” Journal of the Royal Statistical Society, Series B, 71, 927–946. DOI: 10.1111/j.1467-9868.2009.00713.x.
  • Cressie, N., and Wikle, C. K. (2015), Statistics for Spatio-Temporal Data, Hoboken, NJ: Wiley.
  • Cronie, O., and Van Lieshout, M. N. M. (2018), “A Non-Model-based Approach to Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions,” Biometrika, 105, 455–462. DOI: 10.1093/biomet/asy001.
  • Grundy, T. (2020), changepoint.geo: Geometrically Inspired Multivariate Changepoint Detection. R package version 1.0.1.
  • Grundy, T., Killick, R., and Mihaylov, G. (2020), “High-Dimensional Changepoint Detection via a Geometrically Inspired Mapping,” Statistics and Computing, 30, 1155–1166. DOI: 10.1007/s11222-020-09940-y.
  • Hahn, G., Fearnhead, P., and Eckley, I. A. (2020), “Bayesproject: Fast Computation of a Projection Direction for Multivariate Changepoint Detection,” Statistics and Computing, 30, 1691–1705. DOI: 10.1007/s11222-020-09966-2.
  • Horváth, L., and Rice, G. (2014), “Extensions of Some Classical Methods in Change Point Analysis,” Test, 23, 219–255. DOI: 10.1007/s11749-014-0368-4.
  • James, N. A., and Matteson, D. S. (2015), “ecp: An R package for Nonparametric Multiple Change Point Analysis of Multivariate Data,” Journal of Statistical Software, 62, 1–25.
  • Liu, B., Zhou, C., Zhang, X., and Liu, Y. (2020), “A Unified Data-Adaptive Framework for High Dimensional Change Point Detection,” Journal of the Royal Statistical Society, Series B, 82, 933–963. DOI: 10.1111/rssb.12375.
  • Matteson, D. S., and James, N. A. (2014), “A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data,” Journal of the American Statistical Association, 109, 334–345. DOI: 10.1080/01621459.2013.849605.
  • Moradi, M., Montesino-SanMartin, M., Ugarte, M. D., and Militino, A. F. (2022), “Locally Adaptive Change-Point Detection (LACPD) with Applications to Environmental Changes,” Stochastic Environmental Research and Risk Assessment, 36, 251–269. DOI: 10.1007/s00477-021-02083-0.
  • Page, E. (1954), “Continuous Inspection Schemes,” Biometrika, 41, 100–115.
  • Page, E. (1955), “A Test for a Change in a Parameter Occurring at an Unknown Point,” Biometrika, 42, 523–527.
  • Pérez-Goya, U., Montesino-SanMartin, M., Militino, A. F., and Ugarte, M. D. (2022), rsat: Dealing with Multiplatform Satellite Images from Landsat, MODIS, and Sentinel. R package version 0.1.19.
  • Ramsey, J., and Silverman, B. W. (2005), Functional Data Analysis, New York: Springer.
  • Rand, W. (1971), “Objective Criteria for the Evaluation of Clustering Methods,” Journal of the American Statistical Association, 66, 846–850. DOI: 10.1080/01621459.1971.10482356.
  • Sundararajan, R. R., and Pourahmadi, M. (2018), “Nonparametric Change Point Detection in Multivariate Piecewise Stationary Time Series,” Journal of Nonparametric Statistics, 30, 926–956. DOI: 10.1080/10485252.2018.1504943.
  • Toreti, A., Belward, A., Perez-Dominguez, I., Naumann, G. et al. (2019), “The Exceptional 2018 European Water Seesaw Calls for Action on Adaptation,” Earth’s Future, 7, 652–663. DOI: 10.1029/2019EF001170.
  • Truong, C., Oudre, L., and Vayatis, N. (2020), “Selective Review of Offline Change Point Detection Methods,” Signal Processing, 167, 107299. DOI: 10.1016/j.sigpro.2019.107299.
  • Wan, Z., Hook, S., and Hulley, G. (2015), “Mod11c3 modis/terra Land Surface Temperature/Emissivity Monthly l3 Global 0.05 deg cmg v006,” NASA EOSDIS LP DAAC.
  • Wang, T., and Samworth, R. J. (2018), “High Dimensional Change Point Estimation via Sparse Projection,” Journal of the Royal Statistical Society, Series B, 80, 57–83. DOI: 10.1111/rssb.12243.
  • Xiong, L., Jiang, C., Xu, C., Yu, K.-x., Guo, S. (2015), “A Framework of Change-Point Detection for Multivariate Hydrological Series,” Water Resources Research, 51, 8198–8217. DOI: 10.1002/2015WR017677.
  • Zammit-Mangion, A., Dewar, M., Kadirkamanathan, V., and Sanguinetti, G. (2012), “Point Process Modelling of the Afghan War Diary,” Proceedings of the National Academy of Sciences, 109, 12414–12419. DOI: 10.1073/pnas.1203177109.
  • Zhang, N. R., Siegmund, D. O., Ji, H., and Li, J. Z. (2010), “Detecting Simultaneous Changepoints in Multiple Sequences,” Biometrika, 97, 631–645. DOI: 10.1093/biomet/asq025.