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On robustness for spatio-temporal data

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dc.contributor.authorGarcía Pérez, Alfonso
dc.date.accessioned2024-05-20T11:24:48Z
dc.date.available2024-05-20T11:24:48Z
dc.date.issued2022-05-23
dc.description.abstractThe spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept. However, the traditional spatio-temporal variogram estimator, which is commonly employed for these purposes, is extremely sensitive to outliers. We approach this problem in two ways in the paper. First, new robust spatio-temporal variogram estimators are introduced, which are defined as M-estimators of an original data transformation. Second, we compare the classical estimate against a robust one, identifying spatio-temporal outliers in this way. To accomplish this, we use a multivariate scale-contaminated normal model to produce reliable approximations for the sample distribution of these new estimators. In addition, we define and study a new class of M-estimators in this paper, including real-world applications, in order to determine whether there are any significant differences in the spatio-temporal variogram between two temporal lags and, if so, whether we can reduce the number of lags considered in the spatio-temporal analysis.en
dc.description.versionversión final
dc.identifier.doihttps://doi.org/10.3390/math10101785
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/20.500.14468/11927
dc.journal.issue10
dc.journal.titleMathematics
dc.journal.volume10
dc.language.isoen
dc.publisherMDPI
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.keywordsrobust statistics
dc.subject.keywordsspatio-temporal outliers
dc.subject.keywordsvon Mises expansions
dc.subject.keywordssaddlepoint approximations
dc.titleOn robustness for spatio-temporal dataes
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationf1b6d45c-9238-4c82-8f82-ccdb82bd6cd7
relation.isAuthorOfPublication.latestForDiscoveryf1b6d45c-9238-4c82-8f82-ccdb82bd6cd7
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