|Title||Data Dispersion: Now You See it... Now You Don't|
|Publication Type||Working Paper|
|Year of Publication||2010|
|Authors||Sellers, K. F., and G. Shmueli|
|Series Title||Working Paper RHS 06-122|
|Institution||Smith School of Business, University of Maryland|
The most popular method for modeling count data is Poisson regression. When data display over-dispersion, thereby deeming Poisson regression inadequate, typically negative-binomial regression is instead used. We show that count data that appear to be equi-dispersed 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 that allows for group-level dispersion. We illustrate mixed dispersion effects and the proposed methodology via semi-authentic data.