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2014-02-19Diskussionspapier DOI: 10.18452/4503
An Application of Principal Component Analysis on Multivariate Time-Stationary Spatio-Temporal Data
dc.contributor.authorStahlschmidt, Stephan
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorThome, Helmut
dc.date.accessioned2017-06-16T00:58:59Z
dc.date.available2017-06-16T00:58:59Z
dc.date.created2014-06-23
dc.date.issued2014-02-19
dc.date.submitted2014-02-19
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/5155
dc.description.abstractPrincipal component analysis denotes a popular algorithmic technique to dimension reduction and factor extraction. Spatial variants have been proposed to account for the particularities of spatial data, namely spatial heterogeneity and spatial autocorrelation, and we present a novel approach which transfers principal component analysis into the spatio-temporal realm. Our approach, named stPCA, allows for dimension reduction in the attribute space while striving to preserve much of the data's variance and maintaining the data's original structure in the spatio-temporal domain. Additionally to spatial autocorrelation stPCA exploits any serial correlation present in the data and consequently takes advantage of all particular features of spatial-temporal data. A simulation study underlines the superior performance of stPCA if compared to the original PCA or its spatial variants and an application on indicators of economic deprivation and urbanism demonstrates its suitability for practical use.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectPCAeng
dc.subjectdimension reductioneng
dc.subjectspatio-temporal analysiseng
dc.subjectfactor extractioneng
dc.subjecteconomic deprivationeng
dc.subjecturbanismeng
dc.subject.ddc310 Sammlungen allgemeiner Statistiken
dc.subject.ddc330 Wirtschaft
dc.titleAn Application of Principal Component Analysis on Multivariate Time-Stationary Spatio-Temporal Data
dc.typeworkingPaper
dc.identifier.urnurn:nbn:de:kobv:11-100218171
dc.identifier.doihttp://dx.doi.org/10.18452/4503
local.edoc.pages24
local.edoc.type-nameDiskussionspapier
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-year2014
dc.identifier.zdb2195055-6
bua.series.nameSonderforschungsbereich 649: Ökonomisches Risiko
bua.series.issuenumber2014,16

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