Assessing influence in Gaussian long-memory models

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dc.contributor.authorPalma, Wilfredo
dc.contributor.authorBondon, Pascal
dc.contributor.authorTapia, Jose
dc.date.accessioned2024-01-10T12:04:20Z
dc.date.available2024-01-10T12:04:20Z
dc.date.issued2008
dc.description.abstractA statistical methodology for detecting influential observations in long-memory models is proposed. The identification of these influential points is carried out by case-deletion techniques. In particular, a Kullback-Leibler divergence is considered to measure the effect of a subset of observations on predictors and smoothers. These techniques are illustrated with an analysis of the River Nile data where the proposed methods are compared to other well-known approaches such as the Cook and the Mahalanobis distances. (c) 2008 Elsevier B.V. All rights reserved.
dc.fechaingreso.objetodigital2024-04-09
dc.format.extent15 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.csda.2008.01.030
dc.identifier.issn0167-9473
dc.identifier.urihttps://doi.org/10.1016/j.csda.2008.01.030
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75767
dc.identifier.wosidWOS:000257014000023
dc.information.autorucMatemática;Palma W;S/I;100091
dc.issue.numero9
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final4501
dc.pagina.inicio4487
dc.publisherELSEVIER SCIENCE BV
dc.revistaCOMPUTATIONAL STATISTICS & DATA ANALYSIS
dc.rightsacceso restringido
dc.subjectSTATIONARY TIME-SERIES
dc.subjectOUTLIER DETECTION
dc.subjectOUT DIAGNOSTICS
dc.subjectPREDICTION
dc.subjectREGRESSION
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleAssessing influence in Gaussian long-memory models
dc.typeartículo
dc.volumen52
sipa.codpersvinculados100091
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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