Nonsubjective priors via predictive relative entropy regret

DOI10.1214/009053605000000804zbMath1091.62014arXivmath/0605609OpenAlexW1966983554MaRDI QIDQ2493559

Gauri Sankar Datta, Malay Ghosh, Trevor J. Sweeting

Publication date: 21 June 2006

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0605609

zbMATH Keywords

Bartlett correctionpredictive inferencehigher-order asymptoticsimpartial priorsnonsubjective Bayesian inferencerelative entropy loss

Mathematics Subject Classification ID

Bayesian inference (62F15) Statistical aspects of information-theoretic topics (62B10)

Related Items

Asymptotic properties of Bayesian predictive densities when the distributions of data and target variables are different ⋮ A novel relative entropy-posterior predictive model checking approach with limited information statistics for latent trait models in sparse \(2^k\) contingency tables ⋮ Optimal shrinkage estimation of predictive densities under \(\alpha\)-divergences ⋮ Axioms for minimax regret choice correspondences ⋮ Unnamed Item ⋮ Predictive probability matching priors for a certain non-regular model ⋮ Improved minimax predictive densities under Kullback-Leibler loss ⋮ On discrete priors and sparse minimax optimal predictive densities ⋮ On a class of objective priors from scoring rules (with discussion)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Characterization of priors under which Bayesian and frequentist Bartlett corrections are equivalent in the multiparameter case
• Statistical decision theory and Bayesian analysis. 2nd ed
• Reference priors for prediction
• The maximum likelihood prior
• Jeffreys' prior is asymptotically least favorable under entropy risk
• Probability matching priors: Higher order asymptotics
• Bayesian prediction with approximate frequentist validity.
• Partial information reference priors: Derivation and interpretations
• Coverage probability bias, objective Bayes and the likelihood principle
• Information-theoretic asymptotics of Bayes methods
• Exact Minimax Strategies for Predictive Density Estimation, Data Compression, and Model Selection
• Noninformative priors for one parameter of many
• On asymptotic properties of predictive distributions
• Reference priors with partial information
• Goodness of prediction fit
• A new look at the Bayes procedure
• Minimax redundancy for the class of memoryless sources
• On the implementation of local probability matching priors for interest parameters

This page was built for publication: Nonsubjective priors via predictive relative entropy regret