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On the correspondence from Bayesian log-linear modelling to logistic regression modelling with g-priors
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dc.contributor.author | Papathomas, Michail | |
dc.date.accessioned | 2017-05-25T10:30:08Z | |
dc.date.available | 2017-05-25T10:30:08Z | |
dc.date.issued | 2018-03 | |
dc.identifier.citation | Papathomas , M 2018 , ' On the correspondence from Bayesian log-linear modelling to logistic regression modelling with g -priors ' , TEST , vol. 27 , no. 1 , pp. 197-220 . https://doi.org/10.1007/s11749-017-0540-8 | en |
dc.identifier.issn | 1133-0686 | |
dc.identifier.other | PURE: 240790966 | |
dc.identifier.other | PURE UUID: 71b5a0b1-6014-4bb8-ac44-f43a7d527dc4 | |
dc.identifier.other | Scopus: 85019553197 | |
dc.identifier.other | ORCID: /0000-0002-5897-695X/work/58755491 | |
dc.identifier.other | WOS: 000425163500010 | |
dc.identifier.uri | https://hdl.handle.net/10023/10854 | |
dc.description.abstract | Consider a set of categorical variables where at least one of them is binary. The log-linear model that describes the counts in the resulting contingency table implies a specific logistic regression model, with the binary variable as the outcome. Within the Bayesian framework, the g-prior and mixtures of g-priors are commonly assigned to the parameters of a generalized linear model. We prove that assigning a g-prior (or a mixture of g-priors) to the parameters of a certain log-linear model designates a g-prior (or a mixture of g-priors) on the parameters of the corresponding logistic regression. By deriving an asymptotic result, and with numerical illustrations, we demonstrate that when a g-prior is adopted, this correspondence extends to the posterior distribution of the model parameters. Thus, it is valid to translate inferences from fitting a log-linear model to inferences within the logistic regression framework, with regard to the presence of main effects and interaction terms. | |
dc.format.extent | 24 | |
dc.language.iso | eng | |
dc.relation.ispartof | TEST | en |
dc.rights | © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | en |
dc.subject | Categorical variables | en |
dc.subject | Contingency tables | en |
dc.subject | Mixtures of g-priors | en |
dc.subject | Prior correspondence | en |
dc.subject | Posterior correspondence | en |
dc.subject | QA Mathematics | en |
dc.subject | NDAS | en |
dc.subject | BDC | en |
dc.subject.lcc | QA | en |
dc.title | On the correspondence from Bayesian log-linear modelling to logistic regression modelling with g-priors | en |
dc.type | Journal article | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. Statistics | en |
dc.contributor.institution | University of St Andrews. Centre for Research into Ecological & Environmental Modelling | en |
dc.identifier.doi | https://doi.org/10.1007/s11749-017-0540-8 | |
dc.description.status | Peer reviewed | en |
dc.identifier.url | http://arxiv.org/abs/1409.3795 | en |
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