Parametric Empirical Bayes Inference: Theory and Applications

By Carl N. Morris. 1983

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, neutral): “, Morris [1983]).”
A General Method for Robust Bayesian Modeling (2015, background): “…the marginal probability of the data under the original Bayesian model—hierarchical models of the form of Equation 1.2 appear extensively in the empirical Bayes literature (Efron and Morris, 1973, 1975; Morris, 1983; Kass and Steffey, 1989; Efron, 1996; Carlin and Louis, 2000a; Efron, 2010).”
Modeling annotated data (2003, methodology): “With K draws from the prior distribution, however, we are in a good position to take the empirical Bayes perspective [9] and compute a maximum likelihood estimate of η.”
Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach (2004, methodology): “…a unknown break point in time series (see e.g., Ploberger et al., 1989; Bai, 1997; Polasek and Ren, 1997) and to the EB tradition of constructing posterior estimates of the coefficients of a model by plugging in estimates of the parameters of the prior (see Morris, 1983; Berger, 1985; Efron, 1996).”
A multilevel meta-analysis of single-subject experimental design studies (2008, background): “It can be shown that these estimates are typically more accurate than the estimates based on the data from that participant or study only (Morris, 1983).”
Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models (2008, methodology): “However, Basu, Ghosh and Mukerjee (2003) showed that the resulting empirical Bayes interval proposed by Morris (1983a) still has coverage error of O ( n − 1 ).”
Large-scale inference (2010, methodology): “Morris (1983) gives a careful derivation of empirical Bayes confidence intervals such as (1.46), along with an informative discussion of what one should expect from such intervals.”
Estimating heterogeneity variance in meta‐analysis (2013, background): “One of the estimators not belonging to class (2) (being a non-linear function of Y ) is the empirical Bayes estimator that was proposed by Morris (1983).”
Handbook of multilevel analysis (2008, methodology): “Elegant computational formulae have been derived for deletion residuals in the General Linear Model (see Atkinson [2]), and recently by Zewotir and Galpin [63] and Haslett and Dillane [23] also for random coefficient models with uncorrelated random coefficients.”
Bayes empirical bayes inference of amino acid sites under positive selection. (2005, methodology): “Several procedures have been suggested in the statistics literature to correct for the bias in the NEB approach to achieve approximately correct frequentist coverage probabilities (e.g., Morris 1983; Laird and Louis 1987; Carlin and Gelfand 1990).”
On measures of uncertainty of empirical Bayes small-area estimators (2003, methodology): “Morris (1983) and Laird and Louis (1987) are among handful of researchers who addressed this important issue.”
Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model (2005, background): “It appears that Equation 19 originated with Morris (1983), who suggested it as an “approximate” REML estimate (p. 53).”
Conducting Meta-Analyses in R with the metafor Package (2010, background): “…estimator (DerSimonian and Laird 1986; Raudenbush 2009), the Sidik-Jonkman estimator (Sidik and Jonkman 2005a,b), the maximum-likelihood or restricted maximum-likelihood estimator (Viechtbauer 2005; Raudenbush 2009), or the empirical Bayes estimator (Morris 1983; Berkey et al. 1995).”