Statistical analysis of incomplete long-range dependent data

Simple item page
dc.contributor.authorPalma, W
dc.contributor.authorDel Pino, G
dc.date.accessioned2025-01-21T01:31:44Z
dc.date.available2025-01-21T01:31:44Z
dc.date.issued1999
dc.description.abstractThis paper addresses both theoretical and methodological issues related to the prediction of long-memory models with incomplete data. Estimates and forecasts are calculated by means of state space models and the influence of data gaps on the performance of short and long run predictions is investigated. These techniques are illustrated with a statistical analysis of the minimum water levels of the Nile river, a time series exhibiting strong dependency.
dc.fuente.origenWOS
dc.identifier.issn0006-3444
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/97133
dc.identifier.wosidWOS:000084833000020
dc.issue.numero4
dc.language.isoen
dc.pagina.final972
dc.pagina.inicio965
dc.revistaBiometrika
dc.rightsacceso restringido
dc.subjectARFIMA model
dc.subjectincomplete data
dc.subjectlinear predictor
dc.subjectlong-memory
dc.subjectmaximum likelihood
dc.subjectmean square prediction error
dc.subjectstate space system
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleStatistical analysis of incomplete long-range dependent data
dc.typeartículo
dc.volumen86
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas