Data analysis using regression models with missing observations and long-memory: an application study

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dc.contributor.authorIglesias, P
dc.contributor.authorJorquera, H
dc.contributor.authorPalma, W
dc.date.accessioned2024-01-10T13:47:19Z
dc.date.available2024-01-10T13:47:19Z
dc.date.issued2006
dc.description.abstractThe objective of this work is to propose a statistical methodology to handle regression data exhibiting long memory errors and missing values. This type of data appears very often in many areas, including hydrology and environmental sciences, among others. A generalized linear model is proposed to deal with this problem and an estimation strategy is developed that combines both classical and Bayesian approaches. The estimation methodology proposed is illustrated with an application to air pollution data which shows the impact of the long memory in the statistical inference and of the missing values on the computations. From a Bayesian standpoint, genuine priors are considered for the parameters of the model which are justified within the context of the air pollution model derivation. (c) 2005 Elsevier B.V. All rights reserved.
dc.fechaingreso.objetodigital2024-04-09
dc.format.extent16 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.csda.2005.03.007
dc.identifier.eissn1872-7352
dc.identifier.issn0167-9473
dc.identifier.urihttps://doi.org/10.1016/j.csda.2005.03.007
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79257
dc.identifier.wosidWOS:000236021700008
dc.information.autorucMatemática;Jorquera H;S/I;100302
dc.information.autorucMatemática;Palma W;S/I;100091
dc.issue.numero8
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final2043
dc.pagina.inicio2028
dc.publisherELSEVIER
dc.revistaCOMPUTATIONAL STATISTICS & DATA ANALYSIS
dc.rightsacceso restringido
dc.subjectARFIMA model
dc.subjectBayesian estimation
dc.subjectKalman filter
dc.subjectlong memory processes
dc.subjectparameter estimation
dc.subjectregression model
dc.subjectPARTICULATE MATTER
dc.subjectAIR-QUALITY
dc.subjectSANTIAGO
dc.subjectCOARSE
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleData analysis using regression models with missing observations and long-memory: an application study
dc.typeartículo
dc.volumen50
sipa.codpersvinculados100302
sipa.codpersvinculados100091
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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