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Latent class analysis identification of syndromes in Alzheimer's disease: A Bayesian approach

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dc.contributor.author Walsh, Cathal Dominic
dc.date.accessioned 2016-03-02T11:13:40Z
dc.date.available 2016-03-02T11:13:40Z
dc.date.issued 2006
dc.identifier.uri http://hdl.handle.net/10344/4936
dc.description peer-reviewed en_US
dc.description.abstract Latent variable models have been used extensively in the social sciences. In this work a latent class analysis is used to identify syndromes within Alzheimer's disease. The fitting of the model is done in a Bayesian framework, and this is examined in detail here. In particular, the label switching problem is identified, and solutions presented. Graphical summaries of the posterior distribution are included. en_US
dc.language.iso eng en_US
dc.publisher Univerza v Ljubljani, Fakulteta za Druzbene Vede en_US
dc.relation.ispartofseries Metodoloski Zvezki;3 (1), pp. 147-162
dc.subject Alzheimer's disease en_US
dc.subject Bayesian framework en_US
dc.title Latent class analysis identification of syndromes in Alzheimer's disease: A Bayesian approach en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2016-03-02T10:24:45Z
dc.description.version PUBLISHED
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1630545
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed


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