Random Effects Count Models
Random Effects Count Models is a class of statistical models for count data that incorporate unobserved heterogeneity across observational units by introducing random parameters, typically at the individual or group level, to account for within-unit correlation in longitudinal or clustered data.
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7.1K
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168
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114
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2017–2026
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Specification and testing of some modified count data models
John Mullahy · Journal of Econometrics · 1986 · 1.8K citations
Eric I. Benchimol, Çharles N. Bernstein, Alain Bitton et al. · The American Journal of Gastroenterology · 2017 · 314 citations · Full text
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University at Buffalo, State University of New York, United States
2 publications · 843 citations
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Oregon State University
Corvallis, United States
3 h-index
13 publications
2.4K citations

Zoological Society of London
London, United Kingdom
2 h-index
2 publications
1.6K citations
University of South Carolina
Columbia, United States
2 h-index
3 publications
101 citations
Purdue University West Lafayette
West Lafayette, United States
2 h-index
4 publications
1.7K citations
National Institute of Standards and Technology
Gaithersburg, United States
1 h-index
1 publications
38 citations
Escola Nacional de Saúde Pública
Rio de Janeiro, Brazil
1 h-index
7 publications
98 citations

University of Rochester
Rochester, United States
1 h-index
1 publications
33 citations

University of Alberta
Edmonton, Canada
1 h-index
1 publications
314 citations
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2 publications · 97 citations
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2 publications · 732 citations
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2 publications · 1.6K citations
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2 publications · 1.9K citations
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2 publications · 47 citations
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