Mixed Poisson Models

Mixed Poisson Models is a type of count data model that extends the standard Poisson distribution by incorporating random effects or latent variables to account for overdispersion through a mixture formulation.

258

Publications

13.9K

Citations

715

Authors

401

Institutions

Publications per year

2017–2026

60

Authors

715

  1. GE
    Gordon E. Willmot

    University of Waterloo, Canada

    9 publications · 554 citations

    9

    h-index

  2. JF
    Jerald F. Lawless

    University of Waterloo, Canada

    6 publications · 1.9K citations

    6

    h-index

  3. RJ
    Richard J. Cook

    University of Waterloo, Canada

    5 publications · 115 citations

    5

    h-index

  4. DS
    Daniel S. Nagin

    Carnegie Mellon University, United States

    4 publications · 1.8K citations

    4

    h-index

  5. OH
    Ole Hesselager

    University of Copenhagen, Denmark

    4 publications · 140 citations

    4

    h-index

1–5 of 715

Venues

  1. Astin Bulletin

    11 publications · 394 citations

    11

    h-index

  2. Scandinavian Actuarial Journal

    11 publications · 403 citations

    10

    h-index

  3. 8

    h-index

  4. Journal of Applied Probability

    JAP · 7 publications · 229 citations

    7

    h-index

  5. PLoS ONE

    7 publications · 181 citations

    7

    h-index

1–5