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

Authors

189

  1. PK
    Pranab Kumar Sen

    University of North Carolina at Chapel Hill, United States

    7 publications · 155 citations

    7

    h-index

  2. SE
    S. Ejaz Ahmed

    Brock University, Canada

    5 publications · 75 citations

    5

    h-index

  3. CN
    Carl N. Morris

    RAND Corporation, United States

    5 publications · 1.5K citations

    5

    h-index

  4. WE
    William E. Strawderman

    Rutgers, The State University of New Jersey, United States

    5 publications · 146 citations

    5

    h-index

  5. BE
    Bradley Efron

    Stanford University, United States

    5 publications · 1.7K citations

    5

    h-index

1–5 of 189

Venues

  1. The Annals of Statistics

    18 publications · 3.9K citations

    15

    h-index

  2. Journal of the American Statistical Association

    12 publications · 3.2K citations

    12

    h-index

  3. Communication in Statistics- Theory and Methods

    10 publications · 269 citations

    10

    h-index

  4. Technometrics

    7 publications · 995 citations

    7

    h-index

  5. Canadian Journal of Statistics

    CJS · 6 publications · 254 citations

    6

    h-index

1–5