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Activity Number: 255 - Contributed Poster Presentations: Section on Statistical Computing
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #304381
Title: Fitting Flexible Models for Count Data: COM-Poisson Regression, Bivariate, Multinomial and Mixed Models
Author(s): Darcy Steeg Morris* and Kimberly F Sellers
Companies: U.S. Census Bureau and Georgetown University
Keywords: count data; Conway-Maxwell-Poisson distribution; data dispersion

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.

Authors who are presenting talks have a * after their name.

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