Adaptive Shrinkage

Adaptive Shrinkage is a statistical estimation method within the empirical Bayes framework that automatically adjusts the degree of shrinkage applied to parameter estimates based on the data, thereby balancing bias and variance in a way that depends on the underlying signal strength and sparsity patterns.

134

Publications

16.8K

Citations

345

Authors

183

Institutions

Publications per year

2017–2026

41

Authors

345

  1. EI
    Edward I. George

    University of Pennsylvania, United States

    5 publications · 251 citations

    5

    h-index

  2. WP
    Wilfried Philips

    Ghent University, Belgium

    3 publications · 495 citations

    3

    h-index

  3. AP
    Aleksandra Pižurica

    Ghent University, Belgium

    3 publications · 495 citations

    3

    h-index

  4. WK
    Won‐Seok Kang

    Chung-Ang University, South Korea

    3 publications · 70 citations

    3

    h-index

  5. EL
    Eunsung Lee

    Chung-Ang University, South Korea

    3 publications · 70 citations

    3

    h-index

1–5 of 345

Venues

  1. Journal of the American Statistical Association

    11 publications · 482 citations

    11

    h-index

  2. Statistics in Medicine

    3 publications · 55 citations

    3

    h-index

  3. Bioinformatics

    3 publications · 80 citations

    3

    h-index

  4. Econometric Theory

    3 publications · 101 citations

    3

    h-index

  5. IEEE Access

    3 publications · 99 citations

    3

    h-index

1–5