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Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/20148
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dc.contributor.authorFitzpatrick, Benjamin Ren
dc.contributor.authorLamb, Daviden
dc.contributor.authorMengersen, Kerrieen
dc.date.accessioned2017-03-09T16:24:00Z-
dc.date.issued2016-
dc.identifier.citationPLoS One, 11(9), p. 1-19en
dc.identifier.issn1932-6203en
dc.identifier.urihttps://hdl.handle.net/1959.11/20148-
dc.description.abstractModern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment.en
dc.languageenen
dc.publisherPublic Library of Scienceen
dc.relation.ispartofPLoS Oneen
dc.titleUltrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Studyen
dc.typeJournal Articleen
dc.identifier.doi10.1371/journal.pone.0162489en
dcterms.accessRightsGolden
dc.subject.keywordsApplied Statisticsen
dc.subject.keywordsAgricultural Spatial Analysis and Modellingen
local.contributor.firstnameBenjamin Ren
local.contributor.firstnameDaviden
local.contributor.firstnameKerrieen
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.for2008010401 Applied Statisticsen
local.subject.seo2008961402 Farmland, Arable Cropland and Permanent Cropland Soilsen
local.subject.seo2008961403 Forest and Woodlands Soilsen
local.profile.schoolSchool of Science and Technologyen
local.profile.emaildlamb@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20161220-16165en
local.publisher.placeUnited States of Americaen
local.identifier.runningnumbere0162489en
local.format.startpage1en
local.format.endpage19en
local.identifier.scopusid84991704839en
local.peerreviewedYesen
local.identifier.volume11en
local.identifier.issue9en
local.title.subtitleA Digital Soil Mapping Case Studyen
local.access.fulltextYesen
local.contributor.lastnameFitzpatricken
local.contributor.lastnameLamben
local.contributor.lastnameMengersenen
dc.identifier.staffune-id:dlamben
local.profile.orcid0000-0002-2917-2231en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:20346en
dc.identifier.academiclevelAcademicen
local.title.maintitleUltrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Dataen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorFitzpatrick, Benjamin Ren
local.search.authorLamb, Daviden
local.search.authorMengersen, Kerrieen
local.uneassociationUnknownen
local.identifier.wosid000383255200070en
local.year.published2016en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/2a975419-1931-40fe-9f52-35a6ee27676den
local.subject.for2020490501 Applied statisticsen
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.seo2020180603 Evaluation, allocation, and impacts of land useen
local.subject.seo2020180605 Soilsen
local.codeupdate.date2022-02-14T08:48:12.864en
local.codeupdate.epersonrtobler@une.edu.auen
local.codeupdate.finalisedtrueen
local.original.for2020300206 Agricultural spatial analysis and modellingen
local.original.for2020490501 Applied statisticsen
local.original.seo2020180605 Soilsen
local.original.seo2020undefineden
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