Hierarchical spatio-temporal change-point detection

Moradi, Mohammad Mehdi; Cronie, Ottmar; Pérez Goya, Unai; Mateu, Jorge

Hierarchical spatio-temporal change-point detection

dc.contributor.author	Moradi, Mohammad Mehdi
dc.contributor.author	Cronie, Ottmar
dc.contributor.author	Pérez Goya, Unai
dc.contributor.author	Mateu, Jorge
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.date.accessioned	2023-08-22T18:51:04Z
dc.date.available	2023-08-22T18:51:04Z
dc.date.issued	2023
dc.date.updated	2023-08-22T18:42:46Z
dc.description.abstract	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.	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Moradi, M., Cronie, O., Pérez-Goya, U., Mateu, J. (2023) Hierarchical spatio-temporal change-point detection. The American Statistician, 1-11. https://doi.org/10.1080/00031305.2023.2191670.	en
dc.identifier.doi	10.1080/00031305.2023.2191670
dc.identifier.issn	0003-1305
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/46011
dc.language.iso	eng	en
dc.publisher	Taylor and Francis Group	en
dc.relation.ispartof	The American Statistician 2023	en
dc.relation.publisherversion	https://doi.org/10.1080/00031305.2023.2191670
dc.rights	© 2023 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License.	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Clustering	en
dc.subject	Functional data	en
dc.subject	Land surface temperature	en
dc.subject	Multivariate analysis	en
dc.subject	Point patterns	en
dc.subject	Satellite images	en
dc.subject	Trace-variogram	en
dc.title	Hierarchical spatio-temporal change-point detection	en
dc.type	info:eu-repo/semantics/article
dc.type.version	info:eu-repo/semantics/publishedVersion
dspace.entity.type	Publication
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