Fully Bayes Factors With a Generalized g-Prior

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Statistics Papers
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Bayes factor
model selection consistency
ridge regression
singular value decomposition
variable selection
Statistics and Probability
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Maruyama, Yuzo
George, Edward I
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For the normal linear model variable selection problem, we propose selection criteria based on a fully Bayes formulation with a generalization of Zellner’s g-prior which allows for p > n. A special case of the prior formulation is seen to yield tractable closed forms for marginal densities and Bayes factors which reveal new model evaluation characteristics of potential interest.

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2011-01-01
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The Annals of Statistics
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