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dc.contributor.authorLeznik, M.
dc.contributor.authorTofallis, C.
dc.date.accessioned2007-09-26T12:51:42Z
dc.date.available2007-09-26T12:51:42Z
dc.date.issued2005
dc.identifier.citationLeznik , M & Tofallis , C 2005 ' Estimating Invariant Principal Components Using Diagonal Regression ' Business School Working Papers , vol. UHBS 2005-4 , University of Hertfordshire .
dc.identifier.otherPURE: 120821
dc.identifier.otherPURE UUID: d3379080-bda2-4d76-ab8d-b557c7fabea1
dc.identifier.otherdspace: 2299/715
dc.identifier.urihttp://hdl.handle.net/2299/715
dc.description.abstractIn this work we apply the method of diagonal regression to derive an alternative version of Principal Component Analysis (PCA). Diagonal regression was introduced by Ragnar Frisch (the first economics Nobel laureate) in his paper Correlation and Scatter in Statistical Variables (1928). The benefits of using diagonal regression in PCA are that it provides components that are scale-invariant (i.e. changing the units of measurement leads to an equivalent result), and which reflect both the correlation structure of the data set, and the variance structure as well. By contrast PCA based on the correlation matrix will only reflect the correlation structure of the data. The problem is formulated as a generalized eigen-analysis and is demonstrated using a numerical example which highlights some desirable properties of what we call Invariant Principal Components Analysis (IPCA).en
dc.language.isoeng
dc.publisherUniversity of Hertfordshire
dc.relation.ispartofseriesBusiness School Working Papers
dc.rightsOpen
dc.titleEstimating Invariant Principal Components Using Diagonal Regressionen
dc.contributor.institutionDepartment of Marketing and Enterprise
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionSocial Sciences, Arts & Humanities Research Institute
dc.contributor.institutionCentre for Research on Management, Economy and Society
dc.contributor.institutionStatistical Services Consulting Unit
dc.relation.schoolHertfordshire Business School
dcterms.dateAccepted2005
rioxxterms.typeWorking paper
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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