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Activity Number:
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255
- Contributed Poster Presentations: Section on Statistical Computing
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #304381
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Title:
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Fitting Flexible Models for Count Data: COM-Poisson Regression, Bivariate, Multinomial and Mixed Models
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Author(s):
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Darcy Steeg Morris* and Kimberly F Sellers
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Companies:
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U.S. Census Bureau and Georgetown University
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Keywords:
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count data;
Conway-Maxwell-Poisson distribution;
data dispersion
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Abstract:
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Count data is prevalent in many fields of application including economics, demography and epidemiology. The Poisson distribution is the standard go-to distribution, however in practice count data very often exhibit variability inconsistent with the Poisson equi-dispersion assumption. The Conway-Maxwell- (COM-) Poisson distribution allows flexible modeling of count data with over- or under-dispersion. The COM-Poisson distribution is the basis of many extensions for flexible modeling of count data including COM-Poisson regression, a COM-Poisson mixed model, a bivariate COM-Poisson distribution and a COM-multinomial distribution. The utility and flexibility of this COM-Poisson class of modeling techniques is demonstrated using published and forthcoming R packages.
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Authors who are presenting talks have a * after their name.
