Empirical Bayes: Past, Present and Future
From MaRDI portal
Publication:4541350
DOI10.2307/2669771zbMath1072.62511OpenAlexW4233387568MaRDI QIDQ4541350
Thomas A. Louis, Bradley P. Carlin
Publication date: 30 July 2002
Full work available at URL: https://doi.org/10.2307/2669771
Related Items
Distrust in experts and the origins of disagreement, Empirical Bayes estimators in hierarchical models with mixture priors, A general method for robust Bayesian modeling, Designing repetitive screening procedures with imperfect inspections: an empirical Bayes approach, The empirical Bayes estimators of the rate parameter of the inverse gamma distribution with a conjugate inverse gamma prior under Stein's loss function, The empirical Bayes estimators of the mean and variance parameters of the normal distribution with a conjugate normal-inverse-gamma prior by the moment method and the MLE method, Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem, An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control, The empirical Bayes estimators of the variance parameter of the normal distribution with a conjugate inverse gamma prior under Stein’s loss function, The empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior under Stein's loss function, The stress-strength reliability model with component strength under partially accelerated life test, A new class of bivariate Sushila distributions in presence of right-censored and cure fraction, A computational framework for empirical Bayes inference, A loss-based prior for variable selection in linear regression methods, Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments, Empirical Bayes estimators of structural distribution of words in Lithuanian texts, A modern retrospective on probabilistic numerics, Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Uses Software
