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Dilution priors: Compensating for model space redundancy

Edward I. George

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For the general Bayesian model uncertainty framework, the focus of this paper is on the development of model space priors which can compensate for redundancy between model classes, the so-called dilution priors proposed in George (1999). Several distinct approaches for dilution prior construction are suggested. One is based on tessellation determined neighborhoods, another on collinearity adjustments, and a third on pairwise distances between models.

Chapter information

James O. Berger, T. Tony Cai and Iain M. Johnstone, eds., Borrowing Strength: Theory Powering Applications – A Festschrift for Lawrence D. Brown (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2010), 158-165

First available in Project Euclid: 26 October 2010

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Mathematical Reviews number (MathSciNet)

Primary: 62F15: Bayesian inference 62J05: Linear regression

model averaging model selection objective Bayes prior distribution variable selection

Copyright © 2010, Institute of Mathematical Statistics


George, Edward I. Dilution priors: Compensating for model space redundancy. Borrowing Strength: Theory Powering Applications – A Festschrift for Lawrence D. Brown, 158--165, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2010. doi:10.1214/10-IMSCOLL611.

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