%0 Journal Article
%T Hyper-g Priors for Generalized Linear Models
%A Daniel Saban¨¦s Bov¨¦
%A Leonhard Held
%J Statistics
%D 2010
%I arXiv
%R 10.1214/11-BA615
%X 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.
%U http://arxiv.org/abs/1008.1550v1