Combining Possibly Related Estimation Problems

By Bradley Efron, Carl N. Morris. 1973

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Citations in the corpus

Kernel Mean Shrinkage Estimators (2014, methodology): “ the original works on James-Stein estimator were entirely written from the frequentist point of view, it was shown later that James-Stein estimator can be understood as an empirical Bayes estimator (Efron and Morris, 1973a). This result provides a treatment of James-Stein estimator from the Bayesia”
Hierarchical Variational Models (2015, methodology): “The augmentation with a variational prior has strong ties to empirical Bayesian methods, which use data to estimate hyperparameters of a prior distribution (Robbins, 1964; Efron and Morris, 1973).”
A General Method for Robust Bayesian Modeling (2015, methodology): “This simple exchangeable model also connects to James-Stein estimation, a powerful method from frequentist statistics that can be understood as an empirical Bayes procedure (Efron and Morris, 1973; Efron, 2010).”
Group-Linear Empirical Bayes Estimates for a Heteroscedastic Normal Mean (2015, background): “Equivalently, as observed in Efron and Morris (1973b), the James-Stein estimator is an empirical version of the linear Bayes rule (that is, the linear estimator with smallest Bayes risk) when θ is only assumed to have i.”
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification (2017, background): “well studied in the literature: Breiman [4] summarized and referred several related studies [2,11,16,34,35] about the theoretical properties of ensemble learning.”
A Semiparametric Basis for Combining Estimation Problems Under Quadratic Loss (2004, unknown): “George Barnard (1962), as reported in Efron and Morris (1973) When there is uncertainty concerning the appropriate statistical model–estimator to use in representing the data sampling process, we consider a basis for optimally combining estimation problems.”
On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data (2001, unknown): “As in other domains of application, we know that some bene (cid:142) t can be attained if the thousands of parameters are considered simultaneously, rather than in isolation (Efron and Morris, 1973, 1977; Carlin and Louis, 1996).”
A survey of cross-validation procedures for model selection (2009, unknown): “The idea of using CV for model selection arose in the discussion of a paper by Efron and Morris (1973) and in a paper by Geisser (1974).”