James-stein Estimation

James-Stein Estimation is a shrinkage estimation method in statistical inference that improves upon maximum likelihood estimation by jointly adjusting individual parameter estimates toward a common mean, thereby achieving lower overall risk in multivariate normal settings with three or more parameters.

168

Publications

14.1K

Citations

224

Authors

142

Institutions

Publications per year

2017–2026

19

Authors

224

  1. PK
    Pranab Kumar Sen

    University of North Carolina at Chapel Hill, United States

    7 publications · 155 citations

    7

    h-index

  2. CN
    Carl N. Morris

    RAND Corporation, United States

    7 publications · 2K citations

    7

    h-index

  3. WE
    William E. Strawderman

    Rutgers, The State University of New Jersey, United States

    6 publications · 199 citations

    6

    h-index

  4. BE
    Bradley Efron

    Stanford University, United States

    6 publications · 2K citations

    6

    h-index

  5. SE
    S. Ejaz Ahmed

    Brock University, Canada

    5 publications · 75 citations

    5

    h-index

1–5 of 224

Venues

  1. Journal of the American Statistical Association

    16 publications · 3.8K citations

    14

    h-index

  2. The Annals of Statistics

    17 publications · 3.7K citations

    14

    h-index

  3. Communication in Statistics- Theory and Methods

    11 publications · 281 citations

    11

    h-index

  4. Journal of Multivariate Analysis

    7 publications · 152 citations

    7

    h-index

  5. Canadian Journal of Statistics

    CJS · 7 publications · 303 citations

    7

    h-index

1–5