Data Analysis Using Stein's Estimator and its Generalizations

By Bradley Efron, Carl N. Morris. 1975

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Citations in the corpus

A General Method for Robust Bayesian Modeling (2015, methodology): “Fitting the hyperparameter is a type of empirical Bayes estimation (Robbins, 1964; Copas, 1969; Efron and Morris, 1973, 1975; Robbins, 1980; Maritz and Lwin, 1989; Carlin and Louis, 2000b,a).”
Efficient Ranking in Sponsored Search (2011, methodology): “We observe that γ is linked to the concept of shrinkage in Bayesian inference [4], and draw on this connection to empirically estimate the efficient γ for several keywords in Yahoo’s sponsored search market.”
Variational Boosting: Iteratively Refining Posterior Approximations (2016, background): “7 We borrow an example from [4], and estimate the binomial rates of success (batting averages) of baseball players using a hierarchical model.”
Asymptotic Variance and Convergence Rates of Nearly-Periodic Markov Chain Monte Carlo Algorithms (2003, methodology): “Finally, the random-effects model studied analytically by Rosenthal (1996), using the baseball data of Efron and Morris (1975) and Morris (1983), is considered.”
Parametric Empirical Bayes Inference: Theory and Applications (1983, methodology): “The estimate (5.4) of A is not the maximum likelihood estimate of A, but is related to ones used by Fay and Herriot (1979), Carter and Rolph (1974), and "RIDGM" in Dempster, Schatzoff, and Wermuth (1977).”
Seventh IEEE International Conference on Data Mining Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights (background): “a linear combination of the empirical estimator and the common mean . The parameter can be estimated from the formula for the variance of a weighted mean, while can be estimated by the mean of the ’s (optionally weighted by ). Empirical Bayes [ 4 ] suggests that the maximum likelihood estimate of ca”
Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models (2006, background): “See Efron and Morris (1975) and Morris (1983) for discussions of pooling and shrinkage in hierarchical or “empirical Bayes” inference.”
The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation (2020, methodology): “…and Stein (1961) was so revolutionary that it took a while for it to be digested and embraced by the academic community; this process was aided by Efron and Morris (1973, 1975, 1977) who provided a more in-depth analysis of Stein’s shrinkage method, which included the suggestion of alternative…”
Foundations of Linear and Generalized Linear Models (2015, background): “See Efron and Morris (1975). 16Recall that an orthogonal matrix Q is a square matrix having QQT = QTQ = I.”
On measures of uncertainty of empirical Bayes small-area estimators (2003, methodology): “The EB data analysis considered in Efron and Morris (1975) can be related to a small-area estimation problem.”
Evaluation of some random effects methodology applicable to bird ringing data (2002, methodology): “However, overall the shrinkage estimators as a set are to be preferred as being closer to the true S i if the random eþ ects model applies with r 2 > 0 (Efron & Morris, 1975; Casella, 1985).”
Mixed model prediction and small area estimation (2006, methodology): “Efron and Morris (1975) used this data set to demonstrate the performance of their empirical Bayes and limited translation empirical Bayes estimators derived using an exchangeable prior in the presence of an outlying observation.”