Cumulative Logit Models

Cumulative Logit Models is a type of ordinal regression model that estimates the cumulative probability of an outcome being less than or equal to a given category, distinguished from other ordinal models by its use of the logit link function applied to cumulative probabilities across ordered categories.

226

Publications

14.4K

Citations

663

Authors

355

Institutions

Publications per year

2017–2026

50

Authors

663

  1. JR
    J. Richard Landis

    University of Pennsylvania, United States

    4 publications · 209 citations

    4

    h-index

  2. BM
    Bhramar Mukherjee

    University of Michigan, United States

    3 publications · 100 citations

    3

    h-index

  3. AA
    Alan Agresti

    University of Florida, United States

    3 publications · 455 citations

    3

    h-index

  4. IL
    Ivy Liu

    Victoria University of Wellington, New Zealand

    3 publications · 100 citations

    3

    h-index

  5. AC
    Alan Collins

    University of Portsmouth, United Kingdom

    2 publications · 243 citations

    2

    h-index

1–5 of 663

Venues

  1. Statistics in Medicine

    12 publications · 466 citations

    12

    h-index

  2. PLoS ONE

    5 publications · 150 citations

    6

    h-index

  3. SSRN Electronic Journal

    Repository · 5 publications · 111 citations

    5

    h-index

  4. PubMed

    Repository · 3 publications · 61 citations

    3

    h-index

  5. Sociological Methods & Research

    SMR · 3 publications · 43 citations

    3

    h-index

1–5