Bayesian Recurrent Event Models

Bayesian Recurrent Event Models is a statistical modeling framework that extends survival analysis by incorporating Bayesian inference to characterize the intensity and dependence structure of recurrent event processes while accounting for uncertainty in model parameters and event occurrence dynamics.

14

Publications

430

Citations

42

Authors

26

Institutions

Publications per year

2017–2026

4

Authors

42

  1. DS
    Debajyoti Sinha

    Florida State University, United States

    3 publications · 68 citations

    3

    h-index

  2. RM
    Renate Meyer

    University of Auckland, New Zealand

    2 publications · 48 citations

    2

    h-index

  3. TM
    Tapabrata Maiti

    Michigan State University, United States

    2 publications · 51 citations

    2

    h-index

  4. BO
    Bichun Ouyang

    Rush University Medical Center, United States

    2 publications · 47 citations

    2

    h-index

  5. EA
    Edsel A. Peña

    University of South Carolina, United States

    1 publications · 11 citations

    1

    h-index

1–5 of 42

Venues

  1. Statistics in Medicine

    2 publications · 48 citations

    2

    h-index

  2. Biometrical Journal

    2 publications · 45 citations

    2

    h-index

  3. Statistical Methods in Medical Research

    2 publications · 133 citations

    2

    h-index

  4. Biostatistics

    2 publications · 31 citations

    2

    h-index

  5. 1

    h-index

1–5