Objective priors: an introduction for frequentists

DOI10.1214/10-STS338zbMath1246.62045arXiv1108.2120MaRDI QIDQ449810

Malay Ghosh

Publication date: 1 September 2012

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1108.2120

zbMATH Keywords

asymptotic expansionorthogonalitylocation familyscale familyJeffreys priorreference priorslocation-scale familydivergence criterionfirst-order probability matchingleft Haar priorsmultiparameterright Haar priorssecond-order probability matchingshrinkage arguments

Mathematics Subject Classification ID

Bayesian inference (62F15)

Related Items (28)

Overall objective priors ⋮ A defence of subjective fiducial inference ⋮ When should modes of inference disagree? Some simple but challenging examples ⋮ Prior distributions for objective Bayesian analysis ⋮ A Proposal for Informative Default Priors Scaled by the Standard Error of Estimates ⋮ Regularized Aggregation of One-Off Probability Predictions ⋮ Confidence intervals, significance values, maximum likelihood estimates, etc. sharpened into Occam’s razors ⋮ Small sample equivalence tests for exponentiality ⋮ Asymptotic expansion of the posterior density in high dimensional generalized linear models ⋮ Quantifying observed prior impact ⋮ Bayesian inference and the parametric bootstrap ⋮ On divergence measures leading to Jeffreys and other reference priors ⋮ Bayesian benchmark dose analysis ⋮ Objective Bayesian analysis for exponential power regression models ⋮ On an inferential model construction using generalized associations ⋮ Allelic frequency estimation in presence of uncertain priors ⋮ Checking for prior-data conflict using prior-to-posterior divergences ⋮ Predictive probability matching priors for a certain non-regular model ⋮ Inferential Models: A Framework for Prior-Free Posterior Probabilistic Inference ⋮ Objective Bayesian inference with proper scoring rules ⋮ Bayes, reproducibility and the quest for truth ⋮ Penalising model component complexity: a principled, practical approach to constructing priors ⋮ Modern likelihood inference for the maximum/minimum of a bivariate normal vector ⋮ Intrinsic objective Bayesian estimation of the mean of the Tweedie family ⋮ Second-order probability matching priors for the person parameter in unidimensional IRT models ⋮ Multinomial models with linear inequality constraints: overview and improvements of computational methods for Bayesian inference ⋮ Interpreting \(p\)-values and confidence intervals using well-calibrated null preference priors ⋮ Bayesian \(A\)-optimal two-phase designs with a single blocking factor in each phase

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