Hierarchical Spatio-Temporal Change-Point Detection
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
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INVESTIGACIONMetadades
Títol
Hierarchical Spatio-Temporal Change-Point DetectionData de publicació
2023Editor
Taylor and FrancisISSN
0003-1305; 1537-2731Cita bibliogràfica
Mehdi Moradi, Ottmar Cronie, Unai Pérez-Goya & Jorge Mateu (2023) Hierarchical Spatio-Temporal Change-Point Detection, The American Statistician, DOI: 10.1080/00031305.2023.2191670Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://www.tandfonline.com/doi/full/10.1080/00031305.2023.2191670Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
1 Introduction
2 Multivariate Change-Point Detection
3 Hierarchical Spatio-Temporal Change-Point Detection
4 Numerical Evaluation
5 Real Data Analyses
6 Discussion
Supplemental material
Acknowledgements
Refe ... [+]
1 Introduction
2 Multivariate Change-Point Detection
3 Hierarchical Spatio-Temporal Change-Point Detection
4 Numerical Evaluation
5 Real Data Analyses
6 Discussion
Supplemental material
Acknowledgements
References
Full Article Figures & data References Supplemental Citations Metrics Licensing Reprints & Permissions View PDFView EPUB
AbstractFormulae display:MathJax Logo?
Detecting change-points in multivariate settings is usually carried out by analyzing all marginals either independently, via univariate methods, or jointly, through multivariate approaches. The former discards any inherent dependencies between different marginals and the latter may suffer from domination/masking among different change-points of distinct marginals. As a remedy, we propose an approach which groups marginals with similar temporal behaviors, and then performs group-wise multivariate change-point detection. Our approach groups marginals based on hierarchical clustering using distances which adjust for inherent dependencies. Through a simulation study we show that our approach, by preventing domination/masking, significantly enhances the general performance of the employed multivariate change-point detection method. Finally, we apply our approach to two datasets: (i) Land Surface Temperature in Spain, during the years 2000–2021, and (ii) The WikiLeaks Afghan War Diary data. [-]
Publicat a
The American Statistician, 2023Dades relacionades
https://figshare.com/articles/journal_contribution/Hierarchical_Spatio-Temporal_Change-Point_Detection/22587966Drets d'accés
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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