Influence diagnostics in Gaussian spatial-temporal linear models with separable covariance

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dc.catalogadoraba
dc.contributor.authorSaavedra Nievas, Juan Carlos
dc.contributor.authorNicolis, Orietta
dc.contributor.authorGalea Rojas, Manuel Jesús
dc.contributor.authorIbacache Pulgar, German
dc.date.accessioned2024-04-18T21:50:16Z
dc.date.available2024-04-18T21:50:16Z
dc.date.issued2023
dc.description.abstractIn recent decades, there has been a growing interest in modeling spatial-temporal data, which can be found in many fields including geoscience, meteorology and ecology, among many others. The spatial-temporal dependence structure modeling, using a random field approach, is an indispensable tool to estimate the parameters that define this structure. However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. Our proposal is to extend the results of Uribe-Opazo et al. (J Appl Stat 39:615-630, 2012) and De Bastiani et al. (Test 24:322-340, 2015) in the studies of diagnostic techniques to assess the sensitivity of the maximum likelihood estimators to small perturbations in the response variable for the spatial-temporal linear models with separable covariance. The method's viability is illustrated in a simulation study, and in an application to eggs anchovy (Engraulis ringens) abundance data in ichthyoplankton surveys from the northern zone of Chile. The results show that the proposed methodology allows to detect influential observations in a spatial-temporal data set when their covariances are separable.
dc.fechaingreso.objetodigital2024-09-02
dc.fuente.origenWOS
dc.fuente.origenORCID
dc.identifier.doi10.1007/s10651-023-00556-9
dc.identifier.eissn1573-3009
dc.identifier.issn1352-8505
dc.identifier.urihttps://doi.org/10.1007/s10651-023-00556-9
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/85246
dc.identifier.wosidWOS:000961769800001
dc.information.autorucFacultad de Matemáticas; Galea Rojas, Manuel Jesús; 0000-0001-9819-5843; 1008589
dc.issue.numero2
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final155
dc.pagina.inicio131
dc.revistaEnvironmental and Ecological Statistics
dc.rightsacceso restringido
dc.subjectSpatial-temporal data
dc.subjectSeparable covariance function
dc.subjectInfluence diagnostics
dc.subjectAnchovy egg density
dc.subject.ddc510
dc.subject.deweyMatemática física y químicaes_ES
dc.subject.ods02 Zero hunger
dc.subject.odspa02 Hambre cero
dc.titleInfluence diagnostics in Gaussian spatial-temporal linear models with separable covariance
dc.typeartículo
dc.volumen30
sipa.codpersvinculados1008589
sipa.trazabilidadWOS;2023-07-06
sipa.trazabilidadORCID;2024-04-15
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