Bayesian Measurement Error Models

Bayesian Measurement Error Models is a statistical modeling framework that belongs to the class of Bayesian hierarchical models and specifically accounts for uncertainty due to inaccuracies in observed measurements by incorporating prior distributions on true but unobserved covariates or predictors.

208

Publications

10.5K

Citations

580

Authors

344

Institutions

Publications per year

2017–2026

52

Authors

580

  1. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    8 publications · 365 citations

    8

    h-index

  2. PG
    Paul Gustafson

    University of British Columbia, Canada

    6 publications · 211 citations

    6

    h-index

  3. FC
    Frédéric Chevallier

    Laboratoire des Sciences du Climat et de l'Environnement, France

    5 publications · 311 citations

    5

    h-index

  4. HB
    Heleno Bolfarine

    Universidade de São Paulo, Brazil

    4 publications · 57 citations

    4

    h-index

  5. BK
    Bani K. Mallick

    Texas A&M University, United States

    4 publications · 137 citations

    4

    h-index

1–5 of 580

Venues

  1. Statistics in Medicine

    17 publications · 644 citations

    12

    h-index

  2. Biometrics

    6 publications · 232 citations

    6

    h-index

  3. The Journal of the Acoustical Society of America

    JASA · 5 publications · 277 citations

    5

    h-index

  4. American Journal of Epidemiology

    AJE · 4 publications · 538 citations

    4

    h-index

  5. The Astrophysical Journal

    ApJ · 4 publications · 1.4K citations

    4

    h-index

1–5