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dc.contributor.authorSpanos, Aen_US
dc.date.accessioned2017-03-13T19:08:16Z
dc.date.available2017-03-13T19:08:16Z
dc.identifier.urihttp://hdl.handle.net/10919/76644
dc.description.abstractThe Neyman and Scott (1948) model is widely used to demonstrate a serious weakness of the Maximum Likelihood (ML) method: it can give rise to inconsistent estimators. The primary objective of this paper is to revisit this example with a view to demonstrate that the culprit for the inconsistent estimation is not the ML method but an ill-defined statistical model. It is also shown that a simple recasting of this model renders it well-defined and the ML method gives rise to consistent and asymptotically efficient estimators.en_US
dc.relation.urihttp://arxiv.org/abs/1301.6278v1en_US
dc.subjectstat.MEen_US
dc.subjectstat.MEen_US
dc.titleRevisiting the Neyman-Scott model: an Inconsistent MLE or an Ill-defined Model?en_US
dc.typeArticle - Refereed
pubs.organisational-group/Virginia Tech
pubs.organisational-group/Virginia Tech/All T&R Faculty
pubs.organisational-group/Virginia Tech/Science
pubs.organisational-group/Virginia Tech/Science/COS T&R Faculty
pubs.organisational-group/Virginia Tech/Science/Economics


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