A Flexible Model for Count Data: The COM-Poisson Distribution
published: Sept. 26, 2012, recorded: September 2012, views: 512
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Count data arise in many contexts, from word lengths to traffic volume to number of bids in online auctions, and generally in many event-counting applications. Yet, there is a scarcity of statistical models for such data. The Poisson distribution is the most popular distribution for modeling count data, yet it is constrained by its equi-dispersion assumption, making it less than ideal for modeling real data that often exhibit over-dispersion or under-dispersion. The COM-Poisson distribution is a two-parameter generalization of the Poisson distribution that allows for a wide range of over-dispersion and under-dispersion. It also contains the Bernoulli and geometric distributions as special cases, and as a member of the exponential family has useful statistical properties. This distribution's flexibility and special properties have prompted a fast growth of methodological and applied research in various fields. In this talk, I will introduce the COM-Poisson distribution and regression model and mention several other COM-Poisson models that have been published thus far. I will also describe applications of the COM-Poisson in various areas including disclosure limitation, marketing, transportation and linguistics.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !