Robust Mixture Priors

Robust Mixture Priors is a Bayesian prior modeling approach that belongs to the class of hierarchical priors and is specifically characterized by its construction as a finite mixture of competing prior distributions to ensure inferential stability and insensitivity to prior misspecification in clinical trial settings.

8

Publications

725

Citations

24

Authors

20

Institutions

Publications per year

2017–2026

6

Authors

24

  1. ON
    Oliver N. Keene

    GlaxoSmithKline (United Kingdom), United Kingdom

    1 publications · 36 citations

    1

    h-index

  2. MW
    Manuel Wiesenfarth

    Heidelberg University, Germany

    1 publications · 39 citations

    1

    h-index

  3. RG
    Robert G. Price

    Research Triangle Park Foundation, United States

    1 publications · 36 citations

    1

    h-index

  4. EL
    Emmanuel Lesaffre

    KU Leuven, Belgium

    1 publications · 28 citations

    1

    h-index

  5. SB
    Sudip Bose

    George Washington University, United States

    1 publications · 19 citations

    1

    h-index

1–5 of 24

Venues

  1. Pharmaceutical Statistics

    2 publications · 64 citations

    2

    h-index

  2. Biometrics

    2 publications · 505 citations

    2

    h-index

  3. Biostatistics

    1 publications · 87 citations

    1

    h-index

  4. The Annals of Statistics

    1 publications · 19 citations

    1

    h-index

  5. Biometrical Journal

    1 publications · 10 citations

    1

    h-index

1–5