Multinomial Log-linear Models

Multinomial Log-Linear Models is a statistical model that generalizes log-linear modeling to categorical data with more than two outcome categories by specifying linear relationships between the log probabilities of multinomial outcomes and a set of predictor variables, distinguishing it from binary log-linear models through its capacity to handle polytomous responses within a unified framework.

163

Publications

18.2K

Citations

361

Authors

196

Institutions

Publications per year

2017–2026

21

Authors

361

  1. EE
    Edgar Erdfelder

    University of Mannheim, Germany

    4 publications · 714 citations

    4

    h-index

  2. FS
    Frank S. Koppelman

    Northwestern University, United States

    3 publications · 232 citations

    3

    h-index

  3. SJ
    Shelby J. Haberman

    Educational Testing Service, United States

    3 publications · 139 citations

    3

    h-index

  4. MM
    Morten Moshagen

    University of Mannheim, Germany

    3 publications · 360 citations

    3

    h-index

  5. MB
    Michel Bierlaire

    École Polytechnique Fédérale de Lausanne, Switzerland

    3 publications · 241 citations

    3

    h-index

1–5 of 361

Venues

  1. 9

    h-index

  2. Journal of Marketing Research

    5 publications · 2.2K citations

    5

    h-index

  3. Journal of Econometrics

    4 publications · 299 citations

    4

    h-index

  4. Psychometrika

    4 publications · 137 citations

    4

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 4 publications · 145 citations

    4

    h-index

1–5