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dc.contributor.authorFerreira, Guillermo
dc.contributor.authorMateu, Jorge
dc.contributor.authorPorcu, Emilio
dc.date.accessioned2018-05-17T18:15:53Z
dc.date.available2018-05-17T18:15:53Z
dc.date.issued2018
dc.identifier.citationFERREIRA, Guillermo; MATEU, Jorge; PORCU, Emilio. Spatio-temporal analysis with short-and long-memory dependence: a state-space approach. TEST, 2018, vol. 27, núm. 1, p. 221-245ca_CA
dc.identifier.issn1133-0686
dc.identifier.issn1863-8260
dc.identifier.urihttp://hdl.handle.net/10234/174733
dc.description.abstractThis paper deals with the estimation and prediction problems of spatio-temporal processes by using state-space methodology. The spatio-temporal process is represented through an infinite moving average decomposition. This expansion is well known in time series analysis and can be extended straightforwardly in space–time. Such an approach allows easy implementation of the Kalman filter procedure for estimation and prediction of linear time processes exhibiting both short- and long-range dependence and a spatial dependence structure given on the locations. Furthermore, we consider a truncated state-space equation, which allows to calculate an approximate likelihood for large data sets. The performance of the proposed Kalman filter approach is evaluated by means of several Monte Carlo experiments implemented under different scenarios, and it is illustrated with two applications.ca_CA
dc.format.extent25 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringer Berlin Heidelbergca_CA
dc.relation.isPartOfTEST, 2018, vol. 27, núm. 1, p. 221-245ca_CA
dc.rights© Sociedad de Estadística e Investigación Operativa 2017ca_CA
dc.subjectkalman filter algorithmca_CA
dc.subjectsecond-order stationaryca_CA
dc.subjectspace–time geostatisticsca_CA
dc.subjecttime series modelsca_CA
dc.titleSpatio-temporal analysis with short- and long-memory dependence: a state-space approachca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s11749-017-0541-7
dc.relation.projectIDThe first author would like to express his thanks for the support from DIUC 215.014.024-1.0, established by the Universidad de Concepción and Postdoctoral scholarship from Conicyt, Chile, 2014 (Folio 74150023). Jorge Mateu’s research was supported by Grant MTM2013-43917-P from the Spanish Ministry of Science and Education, and Grant P1-1B2015-40 and Emilio Porcu’s research was supported by Fondecyt Regular Project from Ministery of Science and Education, Chile.ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttps://link.springer.com/article/10.1007/s11749-017-0541-7ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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