Random Effects Count Models

Random Effects Count Models is a class of statistical models for count data that incorporate unobserved heterogeneity across observational units by introducing random parameters, typically at the individual or group level, to account for within-unit correlation in longitudinal or clustered data.

56

Publications

7.1K

Citations

168

Authors

114

Institutions

Publications per year

2017–2026

13

Authors

168

  1. PC
    Panagiotis Ch. Anastasopoulos

    University at Buffalo, State University of New York, United States

    2 publications · 843 citations

    2

    h-index

  2. XA
    Xavier A. Harrison

    University of Exeter, United Kingdom

    2 publications · 1.6K citations

    2

    h-index

  3. GP
    G. P. Patil

    Pennsylvania State University, United States

    2 publications · 86 citations

    2

    h-index

  4. DL
    Desmond Leddin

    Dalhousie University, Canada

    1 publications · 314 citations

    1

    h-index

  5. HM
    Hubert Maisonneuve

    University of Geneva, Switzerland

    1 publications · 39 citations

    1

    h-index

1–5 of 168

Venues

  1. Economics Letters

    2 publications · 97 citations

    2

    h-index

  2. Accident Analysis & Prevention

    2 publications · 732 citations

    2

    h-index

  3. PeerJ

    2 publications · 1.6K citations

    2

    h-index

  4. Journal of Econometrics

    2 publications · 1.9K citations

    2

    h-index

  5. 2

    h-index

1–5