Saturday, October 5, 2013: 4:25 PM
207 A (Henry B. Gonzalez Convention Center)
Count data have become widely pervasive in various applied fields requiring data collection, including surveys, environmental studies, disease surveillance, and genetic studies. Classical statistical methods surrounding count data center around the Poisson distribution and associated methodologies, whose assumption is that the mean and variance equal. Real data, however, violate this basic principle in that the dataset displays some form of dispersion[1]. The Conway-Maxwell-Poisson (COM-Poisson) distribution is a flexible alternative for count data that not only contains three classical distributions as special cases, but can more broadly accommodate either over- and under-dispersion. As a result, it has served as a motivating distribution for generalizing many classical statistical methods to allow for dispersion, including regression analysis, control chart theory, and stochastic processes. This talk will highlight some of these areas, and demonstrate their use in vast areas of applications including engineering, business, and biomedical sciences.
[1] Overdispersion refers to the variance being greater than the mean, and underdispersion occurs when the variance is less than the mean of a distribution.