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dc.contributor.authorLee, D.-J. 
dc.contributor.authorZhu, Z.
dc.contributor.authorToscas, P.
dc.date.accessioned2016-06-13T13:12:21Z
dc.date.available2016-06-13T13:12:21Z
dc.date.issued2015-05-05
dc.identifier.issn1180-4009
dc.identifier.urihttp://hdl.handle.net/20.500.11824/120
dc.description.abstractA new methodology is proposed for the analysis, modeling, and forecasting of data collected from a wireless sensor network. Our approach is considered in the framework of a functional data-analysis paradigm where observed data is represented in a functional form. To reduce dimensionality, functional principal components analysis is applied to highlight important underlying characteristics and find patterns of variations. The principal scores are modeled with tensor product smooths that allow for smoothing over space and time. The model is then used for simultaneous spatial prediction at unsampled locations and to forecast future observations. We consider soil temperature data from a wireless sensor network of 50 sensor nodes in two different land types (grassland and forest) observed during 60 consecutive days in private property close to Yass, New South Wales, Australia.
dc.formatapplication/pdf
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectForecasting
dc.subjectFunctional data analysis
dc.subjectFunctional principal components
dc.subjectNon-parametric smoothing
dc.subjectPenalized splines
dc.subjectWireless sensor networks
dc.titleSpatio-temporal functional data analysis for wireless sensor networks data
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1002/env.2344
dc.relation.publisherversionhttp://onlinelibrary.wiley.com/doi/10.1002/env.2344/abstract
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleEnvironmetricsen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España