Data Dispersion: Now You See it... Now You Don't

TitleData Dispersion: Now You See it... Now You Don't
Publication TypeJournal Article
Year of Publication2013
AuthorsSellers, K. F., and G. Shmueli
JournalCommunications in Statistics: Theory and Methods

Poisson regression is the most well-known method for modeling count data. When data
display over-dispersion, thereby violating the underlying equi-dispersion assumption of Pois-
son regression, the common solution is to use negative-binomial regression. We show, how-
ever, that count data that appear to be equi- or over-dispersed may actually stem from a
mixture of populations with different dispersion levels. To detect and model such a mixture,
we introduce a generalization of the Conway-Maxwell-Poisson (COM-Poisson) regression
model that allows for group-level dispersion. We illustrate mixed dispersion effects and the
proposed methodology via semi-authentic data.

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