Nonsubjective priors via predictive relative entropy regret.
441 - 468.
We explore the construction of nonsubjective prior distributions in Bayesian statistics via a posterior predictive relative entropy regret criterion. We carry out a minimax analysis based on a derived asymptotic predictive loss function and show that this approach to prior construction has a number of attractive features. The approach here differs from previous work that uses either prior or posterior relative entropy regret in that we consider predictive performance in relation to alternative nondegenerate prior distributions. The theory is illustrated with an analysis of some specific examples.
|Title:||Nonsubjective priors via predictive relative entropy regret|
|Open access status:||An open access version is available from UCL Discovery|
|Keywords:||nonsubjective Bayesian inference, predictive inference, relative entropy loss, higher-order asymptotics, PARTIAL INFORMATION, MODEL SELECTION, BAYES, DISTRIBUTIONS|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
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