Bayesian Zero-inflated Models

Bayesian Zero-Inflated Models is a Bayesian statistical modeling framework that belongs to the class of mixture models for count data and is specifically characterized by its incorporation of a dual-process structure to simultaneously account for structural zeros and sampling zeros through prior distributions on model parameters.

78

Publications

3.5K

Citations

249

Authors

165

Institutions

Publications per year

2017–2026

28

Authors

249

  1. BN
    Brian Neelon

    Medical University of South Carolina, United States

    3 publications · 227 citations

    3

    h-index

  2. PV
    Penelope Vounatsou

    Swiss Tropical and Public Health Institute, Switzerland

    3 publications · 129 citations

    3

    h-index

  3. GD
    Getachew Dagne

    University of South Florida, United States

    3 publications · 62 citations

    3

    h-index

  4. SM
    Stacia M. DeSantis

    Medical University of South Carolina, United States

    2 publications · 61 citations

    2

    h-index

  5. JR
    Josemar Rodrigues

    Universidade Federal de São Carlos, Brazil

    2 publications · 78 citations

    2

    h-index

1–5 of 249

Venues

  1. Statistics in Medicine

    5 publications · 186 citations

    4

    h-index

  2. 3

    h-index

  3. Biometrics

    3 publications · 187 citations

    3

    h-index

  4. Biometrical Journal

    2 publications · 52 citations

    2

    h-index

  5. Accident Analysis & Prevention

    2 publications · 137 citations

    2

    h-index

1–5