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Improved prediction for a spatio-temporal model

Nowak, Gen; Welsh, Alan

Description

We investigate a framework for improving predictions from models for spatio-temporal data. The framework is based on minimising the mean squared prediction error and can be applied to many models. We applied the framework to a model for monthly rainfall data in the Murray-Darling Basin in Australia. Across a range of prediction situations, we improved the predictive accuracy compared to predictions using only the expectation given by the model. Further, we showed that these improvements in...[Show more]

dc.contributor.authorNowak, Gen
dc.contributor.authorWelsh, Alan
dc.date.accessioned2021-01-12T23:52:44Z
dc.identifier.issn1352-8505
dc.identifier.urihttp://hdl.handle.net/1885/219318
dc.description.abstractWe investigate a framework for improving predictions from models for spatio-temporal data. The framework is based on minimising the mean squared prediction error and can be applied to many models. We applied the framework to a model for monthly rainfall data in the Murray-Darling Basin in Australia. Across a range of prediction situations, we improved the predictive accuracy compared to predictions using only the expectation given by the model. Further, we showed that these improvements in predictive accuracy were maintained even when using a reduced subset of the data for generating predictions
dc.description.sponsorshipThis research was partially supported under the Australian Research Council’s Discovery Projects funding scheme (project number DP180100836).
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherKluwer Academic Publishers
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2020
dc.sourceEnvironmental and Ecological Statistics
dc.subjectKriging
dc.subjectMixed model
dc.subjectPrediction
dc.subjectSpatio-temporal data
dc.titleImproved prediction for a spatio-temporal model
dc.typeJournal article
local.description.notesImported from ARIES
dc.date.issued2020
local.identifier.absfor010405 - Statistical Theory
local.identifier.absfor010401 - Applied Statistics
local.identifier.ariespublicationa383154xPUB13355
local.publisher.urlhttps://link.springer.com
local.type.statusPublished Version
local.contributor.affiliationNowak, Gen, College of Business and Economics, ANU
local.contributor.affiliationWelsh, Alan, College of Business and Economics, ANU
local.description.embargo2099-12-31
dc.relationhttp://purl.org/au-research/grants/arc/DP180100836
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage18
local.identifier.doi10.1007/s10651-020-00447-3
local.identifier.absseo970101 - Expanding Knowledge in the Mathematical Sciences
dc.date.updated2020-11-02T04:17:51Z
CollectionsANU Research Publications

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