Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/31371
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dc.contributor.authorNASSIRI, Vahid-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorVERBEKE, Geert-
dc.contributor.authorBarbosa-Breda, Joao-
dc.date.accessioned2020-07-01T13:34:42Z-
dc.date.available2020-07-01T13:34:42Z-
dc.date.issued2020-
dc.date.submitted2020-06-23T12:03:51Z-
dc.identifier.citationThe American statistician, 74 (2) , p. 125 -136-
dc.identifier.issn0003-1305-
dc.identifier.urihttp://hdl.handle.net/1942/31371-
dc.description.abstractWe consider multiple imputation as a procedure iterating over a set of imputed datasets. Based on an appropriate stopping rule the number of imputed datasets is determined. Simulations and real-data analyses indicate that the sufficient number of imputed datasets may in some cases be substantially larger than the very small numbers that are usually recommended. For an easier use in various applications, the proposed method is implemented in the R package imi.-
dc.description.sponsorshipFunding The authors gratefully acknowledge the financial support from the IAP research network # P7/06 of the Belgian Government (Belgian Science Policy). The research leading to these results has also received funding from the European Seventh Framework Programme FP7 2007–2013 under grant agreement no. 602552. We gratefully acknowledge support from the IWTSBO ExaScience grant. Acknowledgments We are grateful for suggestions made by anonymous referees, which have greatly helped to improve this article. We also wish to thank Ophthalmology Department of UZ Leuven for providing the Leuven Eye Study dataset.-
dc.language.isoen-
dc.publisherAMER STATISTICAL ASSOC-
dc.rights2019 American Statistical Association-
dc.subject.otherCentral limit theorem-
dc.subject.otherIncomplete data-
dc.subject.otherIterative procedure-
dc.subject.otherMissing data-
dc.titleIterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets-
dc.typeJournal Contribution-
dc.identifier.epage136-
dc.identifier.issue2-
dc.identifier.spage125-
dc.identifier.volume74-
local.format.pages12-
local.bibliographicCitation.jcatA1-
dc.description.notesNassiri, V (reprint author), Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium.-
dc.description.notesvahid.nassiri@kuleuven.be-
dc.description.otherNassiri, V (corresponding author), Katholieke Univ Leuven, I BioStat, B-3000 Louvain, Belgium. vahid.nassiri@kuleuven.be-
local.publisher.place732 N WASHINGTON ST, ALEXANDRIA, VA 22314-1943 USA-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.source.typeArticle-
dc.identifier.doi10.1080/00031305.2018.1543615-
dc.identifier.isiWOS:000530956200004-
dc.contributor.orcidMolenberghs, Geert/0000-0002-6453-5448; Barbosa-Breda,-
dc.contributor.orcidJoao/0000-0001-7816-816X; Verbeke, Geert/0000-0001-8430-7576-
dc.identifier.eissn1537-2731-
local.provider.typewosris-
local.uhasselt.uhpubyes-
local.uhasselt.internationalyes-
item.fulltextWith Fulltext-
item.contributorNASSIRI, Vahid-
item.contributorMOLENBERGHS, Geert-
item.contributorVERBEKE, Geert-
item.contributorBarbosa-Breda, Joao-
item.fullcitationNASSIRI, Vahid; MOLENBERGHS, Geert; VERBEKE, Geert & Barbosa-Breda, Joao (2020) Iterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets. In: The American statistician, 74 (2) , p. 125 -136.-
item.accessRightsRestricted Access-
item.validationecoom 2021-
crisitem.journal.issn0003-1305-
crisitem.journal.eissn1537-2731-
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