On the correspondence from Bayesian log-linear modelling to logistic regression modelling with $g$-priors

Michail Papathomas

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arXiv · 1409.3795

On the correspondence from Bayesian log-linear modelling to logistic regression modelling with $g$-priors

Michail Papathomas

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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.

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Michail Papathomas. 2014-09-12. On the correspondence from Bayesian log-linear modelling to logistic regression modelling with $g$-priors. https://arxiv.org/abs/1409.3795

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