Log-linear Models
Log-Linear Models is a class of statistical models for categorical data that represent the logarithm of expected cell counts (or probabilities) in contingency tables as a linear function of parameters associated with the main effects and interactions of the categorical variables, typically treating all variables symmetrically without designating a single response variable.
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Log-linear Analysis
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Choice Reviews Online · 1990 · 35.7K citations
LIBLINEAR: A Library for Large Linear Classification
Rong-En Fan, Kai‐Wei Chang, Cho‐Jui Hsieh et al. · 2008 · 6.6K citations
João Santos Silva, Silvana Tenreyro · The Review of Economics and Statistics · 2006 · 6.1K citations · Full text
Interaction terms in logit and probit models
Chunrong Ai, Edward C. Norton · Economics Letters · 2003 · 5.8K citations
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University of Florida
Gainesville, United States
22 h-index
30 publications
9.2K citations
University of Chicago
Chicago, United States
17 h-index
24 publications
4.1K citations
University of Wisconsin–Madison
Madison, United States
17 h-index
28 publications
1.6K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
20 publications
1.7K citations
Universidade de São Paulo
São Paulo, Brazil
13 h-index
30 publications
1.6K citations

Universidade Federal de Pernambuco
Recife, Brazil
13 h-index
19 publications
1.4K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
12 h-index
17 publications
11.3K citations
University of Southampton
Southampton, United Kingdom
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22 publications
1.3K citations
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SMR · 34 publications · 4K citations
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