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dc.contributor.authorSharifi Far, Serveh
dc.contributor.authorPapathomas, Michail
dc.contributor.authorKing, Ruth
dc.date.accessioned2019-10-17T12:30:12Z
dc.date.available2019-10-17T12:30:12Z
dc.date.issued2021-07-01
dc.identifier.citationSharifi Far , S , Papathomas , M & King , R 2021 , ' Parameter redundancy and the existence of maximum likelihood estimates in log-linear models ' , Statistica Sinica , vol. 31 , no. 3 , pp. 1125-1143 . https://doi.org/10.5705/ss.202018.0100en
dc.identifier.issn1017-0405
dc.identifier.otherPURE: 251876319
dc.identifier.otherPURE UUID: d798ba35-f4fc-4c3c-8eaa-e2d4e037cb8e
dc.identifier.otherORCID: /0000-0002-5897-695X/work/96817488
dc.identifier.otherWOS: 000673915600001
dc.identifier.otherScopus: 85078660729
dc.identifier.urihttps://hdl.handle.net/10023/18698
dc.descriptionThe work of first author is supported by EPSRC PhD grants EP/J500549/1, EP/K503162/1 and EP/L505079/1.en
dc.description.abstractLog-linear models are typically fitted to contingency table data to describe and identify the relationship between different categorical variables. However, the data may include observed zero cell entries. The presence of zero cell entries can have an adverse effect on the estimability of parameters, due to parameter redundancy. We describe a general approach for determining whether a given log-linear model is parameter redundant for a pattern of observed zeros inthe table, prior to fitting the model to the data. We derive the estimable parameters or functions of parameters and also explain how to reduce the unidentifiable model to an identifiable one. Parameter redundant models have a flat ridge in their likelihood function. We further explain when this ridge imposes some additional parameter constraints on the model, which can lead to obtaining unique maximum likelihood estimates for parameters that otherwise would not have been estimable. In contrast to other frameworks, the proposed novel approach informs on those constraints, elucidating the model that is actually being fitted.
dc.language.isoeng
dc.relation.ispartofStatistica Sinicaen
dc.rightsCopyright © 2019 the Author(s). This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.5705/ss.202018.0100en
dc.subjectContingency tableen
dc.subjectExtended maximum likelihood estimateen
dc.subjectParameter redundancyen
dc.subjectSampling zeroen
dc.subjectQA Mathematicsen
dc.subjectT-NDASen
dc.subject.lccQAen
dc.titleParameter redundancy and the existence of maximum likelihood estimates in log-linear modelsen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.5705/ss.202018.0100
dc.description.statusPeer revieweden
dc.identifier.urlhttp://arxiv.org/abs/1902.10009en


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