Here's another neat use of the COM-Poisson distribution (a distribution for count data that includes as special cases Poisson, Bernoulli, and geometric distributions): a count data process! Useful for count data that are over- or under-dispersed.
Our co-authored paper Bridging the Gap: A Generalized Stochastic Process For Count Data (with Li Zhu, Kim Sellers and Darcy Morris) is now published in The American Statistician.

[The link provides free access to the first 50 readers]

My paper Modeling Bimodal Discrete Data Using Conway-Maxwell-Poisson Mixture Models with co-authors Smarajit Bose, Pragya Sur and Paromita Dubey (ISI Kolkata) is finally in print in the ASA's Journal of Business & Economic Statistics. We develop a method for modeling the distribution of bimodal discrete data, such as rankings (on a 5-star scale) and even censored data.

This weekend an important paper that I co-author with Hank Lucas and Mingfeng Lin has been accepted to the prestigious journal Information Systems Research. The paper, entitled "Too Big to Fail: Large Samples and the P-Value Problem" describes a critical challenge that occurs in modeling large samples. Publications in fields such as Information Systems as well as other social sciences have begun to rely on very large samples for testing theories.

On Nov 25 Prof Shmueli presented her work on "A Flexible Regression Model for Count Data" (joint work with Prof Kimberly Sellers, Georgetown University) at the Indian Statistical Institute in Kolkata, India. During her visit with the Bayesian Interdisciplinary Research Unit there, she also visited the Mahalanobis museum.