Limiting the Risk of Bayes and Empirical Bayes Estimators—Part II: The Empirical Bayes Case

By Bradley Efron, Carl N. Morris. 1972

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Citations in the corpus

Kernel Mean Shrinkage Estimators (2014, methodology): “or (Efron and Morris, 1973a). This result provides a treatment of James-Stein estimator from the Bayesian point of view and soon after inspired a considerable number of works in this direction, e.g., Efron and Morris (1972, 1973b, 1975) and later by Berger (1975), Bock (1975), and Hudson (1978). Alt”
Random Coefficient Models (1994, background): “Borrowing strength (Robbins, 1955, Efron and Morris, 1972) is a general principle for efficient inference about each cluster (district) by exploiting the similarity of the clusters.”
Bayesian Estimation and Inference (2015, background): “There has been substantial debate on this issue for over two centuries (see Berger, 2006; Browne & Draper, 2006; Casella, 1985; Efron & Morris, 1972; Howson, 1997; Jeffreys, 1939/1998; Kass & Wasserman, 1996; Lewis, 1980; Suppes, 2007).”
ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION (2016, background): “Efron and Morris (1972) proposed limited translation rules when estimating means of standard normal distributions.”
Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy (2001, methodology): “These estimators are related to the James-Stein (J-S) estimator (JAMES and STEIN, 1962) and can all be understood from an empirical Bayes point of view as pointed out by EFRON and MORRIS (1972).”
Bayesian mixed-effects inference on classification performance in hierarchical data sets (2012, background): “For early contributions to the extensive literature on shrinkage effects, see Stein (1956), James and Stein (1961), and Efron and Morris (1971, 1972).”
Large-scale inference (2010, methodology): “Limited translation estimates (1.37) were developed in Efron and Morris (1972), amid a more general theory of relevance functions, modifications of the James–Stein estimator that allowed individual cases to partially opt out of the overall shrinkage depending on how relevant the other cases…”
Mixed model prediction and small area estimation (2006, unknown): “Following the suggestions of Efron and Morris (1972), Fay and Herriot (1979) considered the following type of adjustment to the EBP: θ̂i = θ̂EBPi , if yi − ci √ Di ≤ θ̂EBPi ≤ yi + ci √ Di, yi − ci √ Di, if θ̂ EBP i < yi − ci √ Di, yi + ci √ Di, if θ̂ EBP i > yi + ci √ Di, where ci is a suitably…”
Governance Matters III : Governance Indicators for 1996, 1998, 2000, and 2002 (2004, methodology): “Unobserved components models were pioneered in economics by Goldberger (1972), and the closely related hierarchical and empirical Bayes models in statistics by Efron and Morris (1971, 1972).”
Stein's Estimation Rule and Its Competitors- An Empirical Bayes Approach (1973, methodology): “In [6], e.”
Revisiting Stein's paradox: multi-task averaging (2014, background): “We are not aware of other work in the multi-task literature that addresses this problem explicitly; most multi-task learning methods are designed for regression, classification, or feature selection, for example, Micchelli and Pontil (2004); Bonilla et al. (2008); Argyriou et al. (2008).”