On the correspondence from Bayesian log-linear modelling to logistic regression modelling with g-priors
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Michail Papathomas [1]
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[1] University of St Andrews
University of St Andrews
Reino Unido
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 27, Nº. 1, 2018 (Ejemplar dedicado a: Special issue on goodness of fit (GOF)), págs. 197-220
- Idioma: inglés
- DOI: 10.1007/s11749-017-0540-8
- Texto completo no disponible (Saber más ...)
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Resumen
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.
