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dc.contributor.authorMcKinley, Trevelyanen
dc.contributor.authorMorters, Michelleen
dc.contributor.authorWood, Jamesen
dc.date.accessioned2015-12-04T17:00:08Z
dc.date.available2015-12-04T17:00:08Z
dc.date.issued2015-01-28en
dc.identifier.citationMcKinley et al. Bayesian Analysis (2015), 10(1), pp. 1-30. doi:10.1214/14-BA884en
dc.identifier.issn1936-0975
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/252874
dc.description.abstractThe use of the proportional odds (PO) model for ordinal regression is ubiquitous in the literature. If the assumption of parallel lines does not hold for the data, then an alternative is to specify a non-proportional odds (NPO) model, where the regression parameters are allowed to vary depending on the level of the response. However, it is often difficult to fit these models, and challenges regarding model choice and fitting are further compounded if there are a large number of explanatory variables. We make two contributions towards tackling these issues: firstly, we develop a Bayesian method for fitting these models, that ensures the stochastic ordering conditions hold for an arbitrary finite range of the explanatory variables, allowing NPO models to be fitted to any observed data set. Secondly, we use reversible-jump Markov chain Monte Carlo to allow the model to choose between PO and NPO structures for each explanatory variable, and show how variable selection can be incorporated. These methods can be adapted for any monotonic increasing link functions. We illustrate the utility of these approaches on novel data from a longitudinal study of individual-level risk factors affecting body condition score in a dog population in Zenzele, South Africa.
dc.description.sponsorshipTJM is supported by Biotechnology and Biological Sciences Research Council grant number BB/I012192/1. MM is supported by a grant from the International Fund for Animal Welfare (IFAW) and the World Society for the Protection of Animals (WSPA), with additional support from the Charles Slater Fund and the Jowett Fund. JW is supported by the Alborada Trust and the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security and the Fogarty International Centre.
dc.languageEnglishen
dc.language.isoenen
dc.publisherInternational Society for Bayesian Analysis
dc.subjectBayesian inferenceen
dc.subjectordinal regressionen
dc.subjectMarkov chain Monte Carloen
dc.subjectreversible-jumpen
dc.subjectBayesian model choiceen
dc.titleBayesian Model Choice in Cumulative Link Ordinal Regression Modelsen
dc.typeArticle
dc.description.versionThis is the final version of the article. It was first available from International Society for Bayesian Analysis via http://dx.doi.org/10.1214/14-BA884en
prism.endingPage30
prism.publicationDate2015en
prism.publicationNameBayesian Analysisen
prism.startingPage1
prism.volume10en
dc.rioxxterms.funderBBSRC
dc.rioxxterms.projectidBB/I012192/1
rioxxterms.versionofrecord10.1214/14-BA884en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-01-28en
dc.contributor.orcidWood, James [0000-0002-0258-3188]
dc.identifier.eissn1931-6690
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idBBSRC (BB/I012192/1)
pubs.funder-project-idInternational Fund for Animal Welfare (IFAW) (unknown)
pubs.funder-project-idInternational Fund for Animal Welfare (IFAW) (unknown)


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