Bayesian variable selection with spherically symmetric priors

Michiel B. De Kock and Hans C. Eggers

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 9, 4250-4263

Abstract: We propose that Bayesian variable selection for linear parametrizations with Gaussian iid likelihoods should be based on the spherical symmetry of the diagonalized parameter space. Our r-prior results in closed forms for the evidence for four examples, including the hyper-g prior and the Zellner–Siow prior, which are shown to be special cases. Scenarios of a single-variable dispersion parameter and of fixed dispersion are studied, and asymptotic forms comparable to the traditional information criteria are derived. A simulation exercise shows that model comparison based on our r-prior gives good results comparable to or better than current model comparison schemes.

Date: 2017
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DOI: 10.1080/03610926.2015.1081945

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