Global and local diagnostic analytics for a geostatistical model based on a new approach to quantile regression

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dc.catalogadoryvc
dc.contributor.authorLeiva, Victor
dc.contributor.authorSánchez, Luis
dc.contributor.authorGalea Rojas, Manuel Jesús
dc.contributor.authorSaulo, Helton
dc.date.accessioned2024-04-16T22:25:21Z
dc.date.available2024-04-16T22:25:21Z
dc.date.issued2020
dc.description.abstractData with spatial dependence are often modeled by geoestatistical tools. In spatial regression, the mean response is described using explanatory variables with georeferenced data. This modeling frequently considers Gaussianity assuming the response follows a symmetric distribution. However, when this assumption is not satisfied, it is useful to suppose distributions with the same asymmetric behavior of the data. This is the case of the Birnbaum-Saunders (BS) distribution, which has been considered in different areas and particularly in environmental sciences due to its theoretical arguments. We propose a geostatistical model based on a new approach to quantile regression considering the BS distribution. Global and local diagnostic analytics are derived for this model. The estimation of model parameters and its local influence are conducted by the maximum likelihood method. Global influence is based on the Cook distance and it is compared to local influence, in both cases to detect influential observations, whose detection and removal can modify the conclusions of a study. We illustrate the proposed methodology applying it to environmental data, which shows this situation changing the conclusions after removing potentially influential observations. A comparison with Gaussian spatial regression is conducted.
dc.description.funderNational Commission for Scientific and Technological Research of the Chilean Government
dc.description.funderResearch Directorate of the Vice President for Research of the Pontificia Universidad Catolica de Chile, Chile
dc.fechaingreso.objetodigital2024-04-16
dc.format.extent10 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1007/s00477-020-01831-y
dc.identifier.eissn1436-3259
dc.identifier.issn1436-3240
dc.identifier.urihttps://doi.org/10.1007/s00477-020-01831-y
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/85152
dc.identifier.wosidWOS:000553712400001
dc.information.autorucFacultad de Matemáticas ; Galea Rojas, Manuel Jesús ; 0000-0001-9819-5843 ; 1008589
dc.issue.numero10
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final1471
dc.pagina.inicio1457
dc.publisherSpringer
dc.revistaStochastic environmental research and risk assessment
dc.rightsacceso restringido
dc.subjectDiagnostic techniques
dc.subjectEnvironmental data
dc.subjectMaximum likelihood method
dc.subjectR software
dc.subjectSpatial models
dc.subject.ddc510
dc.subject.deweyMatemática física y químicaes_ES
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleGlobal and local diagnostic analytics for a geostatistical model based on a new approach to quantile regression
dc.typeartículo
dc.volumen34
sipa.codpersvinculados1008589
sipa.trazabilidadWOS;05-06-2021
sipa.trazabilidadORCID;2024-04-16
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