Modeling heaped count data
Tammy H. Cummings
Institute for Families in Society
University of South Carolina
Columbia, SC
[email protected]
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James W. Hardin
Institute for Families in Society
Department of Epidemiology and Biostatistics
University of South Carolina
Columbia, SC
[email protected]
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Alexander C. McLain
Department of Epidemiology and Biostatistics
University of South Carolina
Columbia, SC
[email protected]
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James R. Hussey
Department of Epidemiology and Biostatistics
University of South Carolina
Columbia, SC
[email protected]
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Kevin J. Bennett
Department of Family and Preventive Medicine
University of South Carolina
Columbia, SC
[email protected]
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Gina M. Wingood
Department of Behavioral Sciences and Health Education
Emory University
Atlanta, GA
[email protected]
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Abstract. We present motivation and new commands for modeling heaped count
data. These data may appear when subjects report counts that are rounded or
favor multiples (digit preference) of a certain outcome, such as the number of
cigarettes reported. The new commands for fitting count regression models (Poisson,
generalized Poisson, negative binomial) are also accompanied by real-world
examples comparing the heaped regression model with the usual regression model
as well as the heaped zero-inflated model with the usual zero-inflated model.
View all articles by these authors:
Tammy H. Cummings, James W. Hardin, Alexander C. McLain, James R. Hussey, Kevin J. Bennett, Gina M. Wingood
View all articles with these keywords:
heapcr, ziheapcr, heapr, ziheapr, count data, heaping, Poisson, generalized Poisson, negative binomial, zero-inflation, interval censored, mixture, rescaled
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