Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13704
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dc.contributor.authorAbrahantes, Jose Cortinas-
dc.contributor.authorAERTS, Marc-
dc.date.accessioned2012-06-01T10:06:43Z-
dc.date.available2012-06-01T10:06:43Z-
dc.date.issued2012-
dc.identifier.citationSTATISTICAL MODELLING, 12 (1), p. 3-27-
dc.identifier.issn1471-082X-
dc.identifier.urihttp://hdl.handle.net/1942/13704-
dc.description.abstractThe presence of one or more covariates that perfectly or almost perfectly predict the outcome of interest (which is referred to as complete or quasi-complete separation, the latter denoting the case when such perfect prediction occurs only for a subset of observations in the data) has been extensively studied in the last four decades. Since 1984, when Albert and Anderson (1984) differentiated between complete and quasi-complete separation, several authors have studied this phenomenon and tried to provide answers or ways of identifying the problem (Lesaffre and Albert, 1989; Firth, 1993; Christmann and Rousseeuw, 2001; Rousseeuw and Christmann, 2003; Allison, 2004; Zorn, 2005; Heinze, 2006). From an estimation perspective, separation leads to infinite coefficients and standard errors, which makes the algorithm collapse or give inappropriate results. As a practical matter, separation forces the analyst to choose from a number of problematic alternatives for dealing with the problem, and in the past the elimination of such problematic variables were common practice to deal with such situations. In the last decade, solutions using penalized likelihood have been proposed, but always dealing with independent binary data. Here we will propose a Bayesian solution to the problem when we deal with clustered binary data using informative priors that are supported by the data and compare it with an alternative procedure proposed by Gelman et al. (2008).-
dc.description.sponsorshipThe authors gratefully acknowledge support from the fund of Scientific Research (FWO, Research Grant G.0151.05) and Belgian IUAP/PAI network P6/03 'Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data' of the Belgian Government (Belgian Science Policy). We would also like to thank the Yves van der Stede (CODA, from Belgium) for providing the data which motivated this research.-
dc.language.isoen-
dc.publisherSAGE PUBLICATIONS LTD-
dc.subject.otherStatistics & Probability; Separation issues; clustered binary data; logistic model; Bayesian analysis; conditional models; penalized likelihood approach-
dc.subject.otherSeparation issues; clustered binary data; logistic model; Bayesian analysis; conditional models; penalized likelihood approach-
dc.titleA solution to separation for clustered binary data-
dc.typeJournal Contribution-
dc.identifier.epage27-
dc.identifier.issue1-
dc.identifier.spage3-
dc.identifier.volume12-
local.format.pages25-
local.bibliographicCitation.jcatA1-
dc.description.notes[Abrahantes, Jose Cortinas] European Food Safety Author EFSA, I-43126 Parma, Italy. [Abrahantes, Jose Cortinas; Aerts, Marc] Hasselt Univ Belgium, Ctr Stat, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Hasselt, Belgium. jose.cortinasabrahantes@efsa.europa.eu-
local.publisher.placeLONDON-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1177/1471082X1001200102-
dc.identifier.isi000302437700002-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.contributorAbrahantes, Jose Cortinas-
item.contributorAERTS, Marc-
item.fullcitationAbrahantes, Jose Cortinas & AERTS, Marc (2012) A solution to separation for clustered binary data. In: STATISTICAL MODELLING, 12 (1), p. 3-27.-
item.validationecoom 2013-
crisitem.journal.issn1471-082X-
crisitem.journal.eissn1477-0342-
Appears in Collections:Research publications
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