James-stein Rule
The James-Stein Rule is an estimation method in statistical inference that belongs to the class of shrinkage estimators, distinguished by its ability to dominate the maximum likelihood estimator in terms of mean squared error when estimating multiple parameters simultaneously under Gaussian assumptions.
130
Publications
11.8K
Citations
189
Authors
115
Institutions
Publications per year
2017–2026
14
Parents
130
Estimation of the Mean of a Multivariate Normal Distribution
Charles Stein · The Annals of Statistics · 1981 · 2.8K citations · Full text
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University of North Carolina at Chapel Hill, United States
7 publications · 155 citations
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Rutgers, The State University of New Jersey, United States
5 publications · 146 citations
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115
University of North Carolina at Chapel Hill
Chapel Hill, United States
6 h-index
6 publications
101 citations
Stanford University
Stanford, United States
6 h-index
8 publications
1.8K citations
RAND Corporation
Santa Monica, United States
4 h-index
4 publications
1.3K citations

Carleton University
Ottawa, Canada
4 h-index
4 publications
65 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
4 h-index
7 publications
580 citations
Purdue University West Lafayette
West Lafayette, United States
3 h-index
4 publications
70 citations
Iowa State University
Ames, United States
3 h-index
3 publications
308 citations
The University of Texas at San Antonio
San Antonio, United States
3 h-index
4 publications
162 citations
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18 publications · 3.9K citations
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12 publications · 3.2K citations
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10 publications · 269 citations
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7 publications · 995 citations
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CJS · 6 publications · 254 citations
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