4 research outputs found
Hyper-g Priors for Generalized Linear Models
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.Comment: 30 pages, 12 figures, poster contribution at ISBA 201