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September 2011 Hyper-$g$ priors for generalized linear models
Daniel Sabanés Bové, Leonhard Held
Bayesian Anal. 6(3): 387-410 (September 2011). DOI: 10.1214/11-BA615

Abstract

We develop an extension of the classical Zellner's $g$-prior to generalized linear models. 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.

Citation

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Daniel Sabanés Bové. Leonhard Held. "Hyper-$g$ priors for generalized linear models." Bayesian Anal. 6 (3) 387 - 410, September 2011. https://doi.org/10.1214/11-BA615

Information

Published: September 2011
First available in Project Euclid: 13 June 2012

zbMATH: 1330.62058
MathSciNet: MR2843537
Digital Object Identifier: 10.1214/11-BA615

Subjects:
Primary: 62C12
Secondary: 62F15, 62J12, 62P10

Rights: Copyright © 2011 International Society for Bayesian Analysis

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Vol.6 • No. 3 • September 2011
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