Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23265
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dc.contributor.authorNASSIRI, Vahid-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorVERBEKE, Geert-
dc.date.accessioned2017-02-28T12:52:14Z-
dc.date.available2017-02-28T12:52:14Z-
dc.date.issued2016-
dc.identifier.citationSmari, W.W. (Ed.). 2016 International Conference on High Performance Computing & Simulation (HPCS), IEEE,p. 736-742-
dc.identifier.isbn9781509020881-
dc.identifier.urihttp://hdl.handle.net/1942/23265-
dc.description.abstractFinite Information Limit (FIL) variance-covariance structures for hierarchical data are introduced and examined: for such data, it is often possible to analyze only a sometimes very small subset, leading to considerable computation time gain, with almost no efficiency loss. A central example is compound-symmetry. A simple approach is proposed to detect this property in a given dataset.-
dc.language.isoen-
dc.publisherIEEE-
dc.rights©2016 IEEE-
dc.subject.otherCompound-symmetry; Correlated Data; Data sub-sampling; Fast and parallel computation-
dc.subject.otherfast and parallel computation; compound-symmetry; correlated data; data subsampling-
dc.titleFinite Information Limit Variance-covariance Structures: Is the Entire Dataset Needed for Analysis?-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsSmari, W.W.-
local.bibliographicCitation.conferencedateJuly 18-22, 2016-
local.bibliographicCitation.conferencename14th International Conference on High Performance Computing & Simulation (HPCS)-
local.bibliographicCitation.conferenceplaceInnsbruck, Austria-
dc.identifier.epage742-
dc.identifier.spage736-
local.format.pages7-
local.bibliographicCitation.jcatC1-
dc.description.notes[Nassiri, Vahid; Verbeke, Geert] Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium. [Molenberghs, Geert] Univ Hasselt, I BioStat, Hasselt, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, Leuven, Belgium. [Verbeke, Geert] Univ Hasselt, Hasselt, Belgium.-
local.publisher.placeNew York-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.identifier.doi10.1109/HPCSim.2016.7568408-
dc.identifier.isi000389590600100-
local.bibliographicCitation.btitle2016 International Conference on High Performance Computing & Simulation (HPCS)-
item.fulltextWith Fulltext-
item.contributorNASSIRI, Vahid-
item.contributorMOLENBERGHS, Geert-
item.contributorVERBEKE, Geert-
item.fullcitationNASSIRI, Vahid; MOLENBERGHS, Geert & VERBEKE, Geert (2016) Finite Information Limit Variance-covariance Structures: Is the Entire Dataset Needed for Analysis?. In: Smari, W.W. (Ed.). 2016 International Conference on High Performance Computing & Simulation (HPCS), IEEE,p. 736-742.-
item.accessRightsRestricted Access-
item.validationecoom 2018-
Appears in Collections:Research publications
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