The maximum likelihood prior

DOI10.1214/aos/1024691462zbMath0927.62023OpenAlexW2007663459MaRDI QIDQ1307089

J. A. Hartigan

Publication date: 14 December 1999

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

Full work available at URL: https://doi.org/10.1214/aos/1024691462

zbMATH Keywords

maximum likelihoodKullback-Leibler distanceJeffreys priorasymptotic admissibilityuninformative priors

Mathematics Subject Classification ID

Bayesian inference (62F15) Admissibility in statistical decision theory (62C15)

Related Items

On minimax optimality of sparse Bayes predictive density estimates, Single observation unbiased priors, Asymptotic properties of Bayesian predictive densities when the distributions of data and target variables are different, Geometry of exponential family with competing risks and censored data, Simultaneous prediction of independent Poisson observables, Asymptotically minimax Bayes predictive densities, A conversation with John Hartigan, Diagnostics of prior-data agreement in applied Bayesian analysis, Moment matching priors, Bayesian prediction based on a class of shrinkage priors for location-scale models, The empirical likelihood prior applied to bias reduction of general estimating equations, Convergence of estimative density: criterion for model complexity and sample size, A general divergence criterion for prior selection, Objective priors: an introduction for frequentists, Discussion on ``Objective priors: an introduction for frequentists by M. Ghosh, Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models, Asymptotical improvement of maximum likelihood estimators on Kullback-Leibler loss, Geometry of an accelerated model with censored data, Unnamed Item, Extensions of the conjugate prior through the Kullback--Leibler separators, Improved minimax predictive densities under Kullback-Leibler loss, Nonsubjective priors via predictive relative entropy regret, On Parametric Bootstrapping and Bayesian Prediction, Shrinkage priors for Bayesian prediction, Nonparametric bootstrap prediction, Minimax predictive density for sparse count data, On the geometry of Bayesian inference, Information theory and superefficiency, A measure of evidence based on the likelihood-ratio statistics, Exact minimax estimation of the predictive density in sparse Gaussian models, Inferred probabilities

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A heuristic method for determining admissibility of estimators - with applications
• Bayes theory
• A third-order optimum property of the maximum likelihood estimator
• Jeffreys' prior is asymptotically least favorable under entropy risk
• Differential geometry of curved exponential families. Curvatures and information loss
• Minimax Estimation and Positive Solutions of Elliptic Equations
• Second-Order Asymptotic Optimality and Positive Solutions of Schrödinger’s Equation
• On asymptotic properties of predictive distributions
• Goodness of prediction fit
• The Asymptotically Unbiased Prior Distribution
• Invariant Prior Distributions
• An Asymptotic Expansion for Posterior Distributions
• Admissible Estimators, Recurrent Diffusions, and Insoluble Boundary Value Problems
• An invariant form for the prior probability in estimation problems