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
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18.2K
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361
Authors
196
Institutions
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2017–2026
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163
Choice Reviews Online · 1990 · 35.7K citations
The Mixed Logit model: The state of practice
David A. Hensher, William H. Greene · Transportation · 2003 · 2K citations
Nonnormaland Categorical Data in Structural Equation Modeling
Sara J. Finney · 2013 · 1.8K citations
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Carnegie Mellon University
Pittsburgh, United States
5 h-index
6 publications
484 citations

University of Minnesota
Minneapolis, United States
4 h-index
6 publications
278 citations

University of Tasmania
Hobart, Australia
4 h-index
22 publications
946 citations
University of Washington
Seattle, United States
4 h-index
7 publications
830 citations
University of Wisconsin–Madison
Madison, United States
4 h-index
6 publications
163 citations

Johns Hopkins University
Baltimore, United States
3 h-index
5 publications
1.2K citations
Michigan State University
East Lansing, United States
3 h-index
5 publications
94 citations
University of Iowa
Iowa City, United States
3 h-index
3 publications
168 citations
1–8 of 196
9 publications · 453 citations
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5 publications · 2.2K citations
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4 publications · 299 citations
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4 publications · 137 citations
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CSDA · 4 publications · 145 citations
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