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arXiv · 1008.1550

Hyper-g Priors for Generalized Linear Models

Daniel Sabanés Bové·Leonhard Held
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Abstract

We develop an extension of the classical Zellner's g-prior to generalized linear models. The prior on the hyperparameter g is handled in a flexible way, so that any continuous proper hyperprior f(g) can be used, giving rise to a large class of hyper-g priors. Connections with the literature are described in detail. A fast and accurate integrated Laplace approximation of the marginal likelihood makes inference in large model spaces feasible. For posterior parameter estimation we propose an efficient and tuning-free Metropolis-Hastings sampler. The methodology is illustrated with variable selection and automatic covariate transformation in the Pima Indians diabetes data set.

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Daniel Sabanés Bové, Leonhard Held. 2010-08-09. Hyper-g Priors for Generalized Linear Models. https://doi.org/10.1214/11-ba615

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