Priors via imaginary training samples of sufficient statistics for objective Bayesian hypothesis testing

DOI10.1007/s40300-019-00159-0zbMath1437.62091OpenAlexW2977231085WikidataQ127215044 ScholiaQ127215044MaRDI QIDQ2175373

Dimitris Fouskakis

Publication date: 29 April 2020

Published in: Metron (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40300-019-00159-0

zbMATH Keywords

sufficient statisticsBayesian hypothesis testingexpected-posterior priorsobjective priorspower-expected-posterior priorsimaginary training samples

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Bayesian inference (62F15) Sufficient statistics and fields (62B05)

Related Items (1)

Power-expected-posterior priors as mixtures of \(g\)-priors in normal linear models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Power-expected-posterior priors for variable selection in Gaussian linear models
• Limiting behavior of the Jeffreys power-expected-posterior Bayes factor in Gaussian linear models
• Compatibility of prior specifications across linear models
• The formal definition of reference priors
• Prior distributions for objective Bayesian analysis
• Power-expected-posterior priors for generalized linear models
• Information consistency of the Jeffreys power-expected-posterior prior in Gaussian linear models
• Hierarchical shrinkage priors for regression models
• Penalising model component complexity: a principled, practical approach to constructing priors
• Training samples in objective Bayesian model selection.
• The Intrinsic Bayes Factor for Model Selection and Prediction
• A comment on D. V. Lindley's statistical paradox
• Expected-posterior prior distributions for model selection
• Generalization of Jeffreys Divergence-Based Priors for Bayesian Hypothesis Testing
• On the use of Non-Local Prior Densities in Bayesian Hypothesis Tests
• Bayesian Hypothesis Testing: A Reference Approach
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion

This page was built for publication: Priors via imaginary training samples of sufficient statistics for objective Bayesian hypothesis testing