Objective Bayesian model selection in Gaussian graphical models

C. M. Carvalho and J. G. Scott

Biometrika, 2009, vol. 96, issue 3, 497-512

Abstract: This paper presents a default model-selection procedure for Gaussian graphical models that involves two new developments. First, we develop a default version of the hyper-inverse Wishart prior for restricted covariance matrices, called the hyper-inverse Wishart g-prior, and show how it corresponds to the implied fractional prior for selecting a graph using fractional Bayes factors. Second, we apply a class of priors that automatically handles the problem of multiple hypothesis testing. We demonstrate our methods on a variety of simulated examples, concluding with a real example analyzing covariation in mutual-fund returns. These studies reveal that the combined use of a multiplicity-correction prior on graphs and fractional Bayes factors for computing marginal likelihoods yields better performance than existing Bayesian methods. Copyright 2009, Oxford University Press.

Date: 2009
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Citations: View citations in EconPapers (21)

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