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2017-09-12Diskussionspapier DOI: 10.18452/18744
Spatial Functional Principal Component Analysis with Applications to Brain Image Data
dc.contributor.authorLi, Yingxing
dc.contributor.authorHuang, Chen
dc.contributor.authorHärdle, Wolfgang Karl
dc.date.accessioned2018-01-24T14:43:16Z
dc.date.available2018-01-24T14:43:16Z
dc.date.issued2017-09-12
dc.identifier.urihttp://edoc.hu-berlin.de/18452/19457
dc.description.abstractThis paper considers a fast and effective algorithm for conducting functional principle component analysis with multivariate factors. Compared with the univariate case, our approach could be more powerful in revealing spatial connections or extracting important features in images. To facilitate fast computation, we connect Singular Value Decomposition with penalized smoothing and avoid estimating a huge dimensional covariance operator. Under regularity assumptions, the results indicate that we may enjoy the optimal convergence rate by employing the smoothness assumption inherent to functional objects. We apply our method on the analysis of brain image data. Our extracted factors provide excellent recovery of the risk related regions of interests in human brain and the estimated loadings are very informative in revealing the individual risk attitude.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectPrincipal Component Analysiseng
dc.subjectPenalized Smoothingeng
dc.subjectAsymptoticseng
dc.subjectfunctional Magnetic Resonance Imaging (fMRI)eng
dc.subject.ddc330 Wirtschaft
dc.titleSpatial Functional Principal Component Analysis with Applications to Brain Image Data
dc.typeworkingPaper
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/19457-2
dc.identifier.doihttp://dx.doi.org/10.18452/18744
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages31
local.edoc.type-nameDiskussionspapier
local.edoc.institutionWirtschaftswissenschaftliche Fakultät
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2017
local.edoc.container-issue24
local.edoc.container-erstkatid2195055-6

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