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dc.contributor.authorTunnicliffe-Wilson, Granville
dc.date.accessioned2008-03-04T12:45:04Z
dc.date.available2008-03-04T12:45:04Z
dc.date.issued1984-03
dc.identifier.issn0210-8054 (versió paper)
dc.identifier.urihttp://hdl.handle.net/2099/4465
dc.description.abstractThe paper reviews the statistical methods of time series analysis used in a selection of papers from respected scientific journals. In particular, problems are considered in the search for cycles, the use of regression to establish causal links between variables, transfer function modelling and the use of filtering to extract componentes of time series. An attempt is made to assess how useful the ideas of ARMA and Transfer Function modelling might be in improving the efficiency of statistical inference in these contexts.
dc.format.extentp. 9-19
dc.language.isoeng
dc.publisherUniversitat Politècnica de Barcelona. Centre de Càlcul
dc.relation.ispartofQüestiió. 1984, vol.8, núm.1
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/es/
dc.subject.otherInference
dc.subject.otherParametric modelling
dc.subject.otherARMA models
dc.subject.otherTransfer function models
dc.subject.otherLinear filtering
dc.subject.otherClimatological time series
dc.titleProblems in scientific time series analysis
dc.typeArticle
dc.subject.lemacInferència
dc.subject.lemacProcessos estocàstics
dc.subject.amsClassificació AMS::62 Statistics::62M Inference from stochastic processes
dc.rights.accessOpen Access
local.ordre3


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