The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.
Parametric Empirical Bayes Inference: Theory and Applications
Abstract
This article reviews the state of multiparameter shrinkage estimators with emphasis on the empirical Bayes viewpoint, particularly in the case of parametric prior distributions. Some successful applications of major importance are considered. Recent results concerning estimates of error and confidence intervals are described and illustrated with data.
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“The highly parallel nature of gene expression experiments lends itself to a particular class of statistical methods, called parametric empirical Bayes, that borrow information between genes in a dynamic way (14,15).”
From limma powers differential expression analyses for RNA-sequencing and microarray studies (2015) · cites this paper as [15]
Citations
Cites
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881
Journal of the Royal Statistical Society.
(1923) · The Economic Journal · doi:10.1093/ej/33.130.269 · Cited by 3,289
The Predictive Sample Reuse Method with Applications
Geisser, S. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10479865 · Cited by 2,748
Estimation with Quadratic Loss
James, W., & Stein, C. M. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-0919-5_30 · Cited by 2,319
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data
Fay, R. E., & Herriot, R. A. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10482505 · Cited by 1,283
Stein's Estimation Rule and its Competitors—An Empirical Bayes Approach
Efron, B., & Morris, C. (1973) · Journal of the American Statistical Association · doi:10.1080/01621459.1973.10481350 · Cited by 981
Sampling and Bayes' Inference in Scientific Modelling and Robustness
Box, G. E. P. (1980) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2982063 · Cited by 905
Data Analysis Using Stein's Estimator and its Generalizations
Efron, B., & Morris, C. N. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10479864 · Cited by 863
An Empirical Bayes Approach to Statistics
Robbins, H. E. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-0919-5_26 · Cited by 858
Research works printed but not linked: 8
- Berger J., 1983, Robustness in Bayesian Statistics
- Efron B., 1975, Advances in Mathematics, 16, 259
- Gini C., 1911, Metron, 15, 133
- Jackson D. A., 1970, Biometrika, 57, 439
- Morris C., 1983, The Annals of Statistics, 11
- Morris, C. and Van Slyke, L. “Empirical Bayes Methods for Pricing Insurance Classes,”. Proceedings of the Business and Economics Statistics Section. pp.579–582. American Statistical Association.
- van der Merwe A. J., 1981, “Confidence Intervals for a Multivariate Normal Mean in the Case of Empirical Bayes Estimation Using Pearson Curves and Normal Approximations,”
- Whitney, A. W. “The Theory of Experience Rating,”. Proceedings of the Casualty Actuarial Society. Vol. IV, pp.274–292.
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“For a discussion of the estimation of normal means in the presence of unequal weights as well as unequal variances, see Morris (1983).”
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“Although it is difficult to derive the null distribution of T analytically, we can use parametric bootstrap to simulate its null distribution based on (4) and then estimate the P-value of the test.”
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“This step was followed by the eBayes function, which uses an empirical Bayes method (Efron and Morris 1973; Morris 1983) to calculate log fold change in normalized read counts and to determine significance between each group identified by the contrast (contrast order: expression in gut relative to…”
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Variation in racial disparities in police use of force
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“Although 12 these general techniques are not new (Morris, 1983), there almost no work on extending this estimator to a setting where we are interested in estimates of a “treatment” on only a subsample of the population, e.g. a department’s propensity to use more intense force against black and…”
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“Seidel and Shavelson’s (2007) meta-analytic study, which adopted the teaching component classification developed by Scheerens and Bosker (1997) and the teaching-learning component framework suggested by Bolhuis (2003), found the highest effect sizes for time for learning and differentiation/adaptive instruction (both .”
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“In the first stage residual heterogeneity was estimated using one of the following estimators: the Hunter-Schmidt estimator (Hunter and Schmidt, 2004) – “HS”, the Hedges estimator (Hedges and Olkin, 1985; Raudenbush, 2009) – “HE”, the DerSimonian-Laird estimator (DerSimonian and Laird 1986; Raudenbush, 2009) – “DL”, t…”
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“…full Bayesian estimation (Carlin and Louis 2000a), the EB approach based on means, called here simply the EB approach (Efron andMorris 1973, 1975; Morris 1983; Maritz and Lwin 1989; Carlin and Louis 2000a, 2000b), maximum likelihood estimation (Hoijtink and Boomsma 1995; Warm 1989), prediction…”
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“7 Conclusion As stated in [13], the concept of Bayesian shrinkage estimation is not limited to a normally distributed random variable or a member of the exponential family.”
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“The majority of the MLM literature is empirical Bayesian ( Morris 1983; Raudenbush and Bryk 2002).”
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Comment: Microarrays, Empirical Bayes and the Two-Groups Model
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STEIN CONFIDENCE SETS BASED ON NON-ITERATED AND ITERATED PARAMETRIC BOOTSTRAPS
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“This, to some extent, renders the approach empirical Bayesian (Casella 1985; Morris 1983).”
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“…Linear Mixed Model Encoder (GLMM) [11] 3 Hashing Encoder (Hash) [12] 8 Helmert Encoder (Helmert) [6] 81 James-Stein Encoder (James-Stein) [13] 3 Leave One Out Encoder (LOOE) [14] 3 M-estimate Encoder (MEestimate) [15] 3 One Hot Encoder (One Hot) [6] 84 Ordinal Encoder (Ordinal) [6] 3…”
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“Starting from the work of Cox (1975) and Morris (1983), researchers proposed numerous methods based on analytical derivations (e.g. Basu et al., 2003; Kubokawa, 2010; Yoshimori & Lahiri, 2014) and resampling (e.g. Hall & Maiti, 2006; Chatterjee et al., 2008).”
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“The exponential family of distribution is commonly used conjugate priors (Morris, 1983).”
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“In this respect, common estimation approaches used in the statistical practice are the empirical best linear unbiased prediction (EBLUP) [15], the empirical Bayesian (EB) [16], and the hierarchical Bayesian (HB) methods [17].”
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“The Empirical Bayes approach [10] is adopted in implementations of LDA model which, ever since it was designed, has been widely applied to collaborative filtering, content-based image retrieval and bioinformatics.”
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“We refer the reader to the surveys and monographs on the theory and practice of EB [Morris, 1983, Casella, 1985, Zhang, 2003, Efron, 2014, Maritz and Lwin, 2018, Efron, 2021].”
Statistical analysis of hierarchical stochastic models: Examples and approaches
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BackgroundMethodInfluentialImproved estimation of instantaneous arrival rates via Empirical Bayes
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“Morris, C. N. (1983b).”
Empirical bayesInterval estimates: An application to geographical epidemiology
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Tango, T. (1988) · Ouyou toukeigaku · doi:10.5023/jappstat.17.81 · Cited by 3
A Bayesian model for inference on population proportions
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Explaining heterogeneity in meta-analysis: a comparison of methods
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Hahn, S., Williamson, P. R., Hutton, J. L., Garner, P., & Flynn, E. V. (2000) · Statistics in Medicine · doi:10.1002/1097-0258(20001230)19:24
Multiple imputation of missing blood pressure covariates in survival analysis
van Buuren, S., Boshuizen, H. C., & Knook, D. L. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19990330)18:6
Metadata
- Authors
- Morris, C. N.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Morris, Carl N.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1983-03
- Volume
- 78
- Issue
- 381
- Pages
- 47-55
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1983607152
- Cited by
- 1,448
- References
- 36
- Citations by year
- 2026: 32 · 2025: 30 · 2024: 52 · 2023: 42 · 2022: 40 · 2021: 51 · 2020: 62 · 2019: 44 · 2018: 49 · 2017: 43 · 2016: 54 · 2015: 63 · 2014: 66 · 2013: 49 · 2012: 48
- FWCI
- 24.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- parametric empirical Bayes, confidence intervals, shrinkage estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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