Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25871
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dc.contributor.authorHERMANS, Lisa-
dc.contributor.authorNASSIRI, Vahid-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorKenward, Michael G.-
dc.contributor.authorVAN DER ELST, Wim-
dc.contributor.authorAERTS, Marc-
dc.contributor.authorVERBEKE, Geert-
dc.date.accessioned2018-04-12T12:02:05Z-
dc.date.available2018-04-12T12:02:05Z-
dc.date.issued2018-
dc.identifier.citationCommunications in statistics. Simulation and computation, 47 (5),p. 1492-1505-
dc.identifier.issn0361-0918-
dc.identifier.urihttp://hdl.handle.net/1942/25871-
dc.description.abstractThis article is concerned with statistically and computationally efficient estimation in a hierarchical data setting with unequal cluster sizes and an AR(1) covariance structure. Maximum likelihood estimation for AR(1) requires numerical iteration when cluster sizes are unequal. A near optimal non-iterative procedure is proposed. Pseudo-likelihood and split-sample methods are used, resulting in computing weights to combine cluster size specific parameter estimates. Results show that the method is statistically nearly as efficient as maximum likelihood, but shows great savings in computation time.-
dc.description.sponsorshipFinancial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. The research leading to these results has also received funding from the European Seventh Framework programme FP7 2007–2013 under grant agreement No. 602552. The authors gratefully acknowledge support from the IWT-SBO ExaScience grant.-
dc.language.isoen-
dc.rights© Taylor & Francis Group, LLC-
dc.subject.othermaximum likelihood; pseudo-likelihood; unequal cluster size-
dc.titleFast, Closed-form, and Efficient Estimators for Hierarchical Models with AR(1) Covariance and Unequal Cluster Sizes-
dc.typeJournal Contribution-
dc.identifier.epage1505-
dc.identifier.issue5-
dc.identifier.spage1492-
dc.identifier.volume47-
local.format.pages14-
local.bibliographicCitation.jcatA1-
dc.description.notesHermans, L (reprint author), Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. lisa.hermans@uhasselt.be-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.statusIn Press-
dc.identifier.doi10.1080/03610918.2017.1316395-
dc.identifier.isi000434682800017-
item.validationecoom 2019-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.fullcitationHERMANS, Lisa; NASSIRI, Vahid; MOLENBERGHS, Geert; Kenward, Michael G.; VAN DER ELST, Wim; AERTS, Marc & VERBEKE, Geert (2018) Fast, Closed-form, and Efficient Estimators for Hierarchical Models with AR(1) Covariance and Unequal Cluster Sizes. In: Communications in statistics. Simulation and computation, 47 (5),p. 1492-1505.-
item.contributorHERMANS, Lisa-
item.contributorNASSIRI, Vahid-
item.contributorMOLENBERGHS, Geert-
item.contributorKenward, Michael G.-
item.contributorVAN DER ELST, Wim-
item.contributorAERTS, Marc-
item.contributorVERBEKE, Geert-
crisitem.journal.issn0361-0918-
crisitem.journal.eissn1532-4141-
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