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2020-01-06Zeitschriftenartikel DOI: 10.3390/jintelligence8010003
Ergodic Subspace Analysis
dc.contributor.authorvon Oertzen, Timo
dc.contributor.authorSchmiedek, Florian
dc.contributor.authorVoelkle, Manuel C.
dc.date.accessioned2020-10-08T07:15:57Z
dc.date.available2020-10-08T07:15:57Z
dc.date.issued2020-01-06none
dc.date.updated2020-03-06T11:27:07Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/22706
dc.description.abstractProperties of psychological variables at the mean or variance level can differ between persons and within persons across multiple time points. For example, cross-sectional findings between persons of different ages do not necessarily reflect the development of a single person over time. Recently, there has been an increased interest in the difference between covariance structures, expressed by covariance matrices, that evolve between persons and within a single person over multiple time points. If these structures are identical at the population level, the structure is called ergodic. However, recent data confirms that ergodicity is not generally given, particularly not for cognitive variables. For example, the g factor that is dominant for cognitive abilities between persons seems to explain far less variance when concentrating on a single person's data. However, other subdimensions of cognitive abilities seem to appear both between and within persons; that is, there seems to be a lower-dimensional subspace of cognitive abilities in which cognitive abilities are in fact ergodic. In this article, we present ergodic subspace analysis (ESA), a mathematical method to identify, for a given set of variables, which subspace is most important within persons, which is most important between person, and which is ergodic. Similar to the common spatial patterns method, the ESA method first whitens a joint distribution from both the between and the within variance structure and then performs a principle component analysis (PCA) on the between distribution, which then automatically acts as an inverse PCA on the within distribution. The difference of the eigenvalues allows a separation of the rotated dimensions into the three subspaces corresponding to within, between, and ergodic substructures. We apply the method to simulated data and to data from the COGITO study to exemplify its usage.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectergodicityger
dc.subjectdimension reductionger
dc.subjectergodic subspace analysisger
dc.subjectcognitionger
dc.subject.ddc150 Psychologienone
dc.subject.ddc610 Medizin und Gesundheitnone
dc.titleErgodic Subspace Analysisnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/22706-5
dc.identifier.doi10.3390/jintelligence8010003none
dc.identifier.doihttp://dx.doi.org/10.18452/22027
dc.type.versionpublishedVersionnone
local.edoc.container-titleJournal of Intelligencenone
local.edoc.pages18none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionLebenswissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameMDPInone
local.edoc.container-publisher-placeBaselnone
local.edoc.container-volume8none
local.edoc.container-issue1none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber3none
dc.identifier.eissn2079-3200
local.edoc.affiliationvon Oertzen, Timo; Department of Psychology, University of the Federal Forces Munich, 85579 Neubiberg, Germany, timo@unibw.de; Max Planck Institute for Human Development, 14195 Berlin, Germany, timo@unibw.denone
local.edoc.affiliationSchmiedek, Florian; DIPF | Leibniz Institute for Research and Information in Education, 60323 Frankfurt am Main, Germany, schmiedek@dipf.denone
local.edoc.affiliationVoelkle, Manuel C.; Department of Psychology, Humboldt-Universität Berlin, 10117 Berlin, Germany, manuel.voelkle@hu-berlin.denone

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