6533b82bfe1ef96bd128ce6b
RESEARCH PRODUCT
Criteria for Bayesian model choice with application to variable selection
Anabel ForteGonzalo García-donatoJames O. BergerMaria J. Bayarrisubject
Statistics and ProbabilityMathematical optimization62C10Model selectiong-priorLinear modelMathematics - Statistics TheoryFeature selectionStatistics Theory (math.ST)Model selectionBayesian inferenceObjective model62J05Prior probability62J15FOS: MathematicsStatistics Probability and Uncertaintyobjective BayesSelection (genetic algorithm)variable selectionMathematicsdescription
In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propose differing prior choices. We first formalize the most general and compelling of the various criteria that have been suggested, together with a new criterion. We then illustrate the potential of these criteria in determining objective model selection priors by considering their application to the problem of variable selection in normal linear models. This results in a new model selection objective prior with a number of compelling properties.
year | journal | country | edition | language |
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2012-06-01 | The Annals of Statistics |