Bayesian Decision-theoretic Sample Size

Bayesian Decision-Theoretic Sample Size is a formal statistical framework that determines the optimal sample size by minimizing the expected posterior loss under a specified decision-theoretic criterion within a Bayesian inferential context.

28

Publications

1.2K

Citations

49

Authors

39

Institutions

Publications per year

2017–2026

1

Authors

49

  1. PL
    Paul L. Canner

    Maryland Medical Research Institute, United States

    2 publications · 82 citations

    2

    h-index

  2. DA
    Donald A. Berry

    The University of Texas MD Anderson Cancer Center, United States

    2 publications · 120 citations

    2

    h-index

  3. RL
    Roger Lewis

    UCLA Medical Center, United States

    2 publications · 120 citations

    2

    h-index

  4. BW
    Byron Wm. Brown

    Stanford University, United States

    1 publications · 41 citations

    1

    h-index

  5. SG
    Stephen G. Walker

    University of Kent, United Kingdom

    1 publications · 15 citations

    1

    h-index

1–5 of 49

Venues

  1. 5

    h-index

  2. Journal of Statistical Planning and Inference

    4 publications · 132 citations

    4

    h-index

  3. 4

    h-index

  4. Statistics in Medicine

    3 publications · 199 citations

    3

    h-index

  5. Journal of Biopharmaceutical Statistics

    JBS · 2 publications · 40 citations

    2

    h-index

1–5