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Hyper-g priors for generalized linear models


Sabanés Bové, D; Held, L (2011). Hyper-g priors for generalized linear models. Bayesian Analysis, 6(3):387-410.

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 effcient and tuning-free Metropolis-Hastings sampler. The methodology is illustrated with variable selection and automatic covariate transformation in the Pima Indians diabetes data set.

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 effcient 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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10 citations in Web of Science®
25 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2011
Deposited On:29 Dec 2011 13:24
Last Modified:05 Apr 2016 15:15
Publisher:International Society for Bayesian Analysis
ISSN:1931-6690
Publisher DOI:https://doi.org/10.1214/11-BA615
Official URL:http://ba.stat.cmu.edu/journal/2011/vol06/issue03/sabanes.pdf

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