Count Data Model

Shmueli et al. (2005) revived a useful discrete distribution called the COM-Poisson (the Conway–Maxwell–Poisson) and introduced its statistical and probabilistic properties. This distribution is a two-parameter extension of the Poisson distribution that generalizes some well-known discrete distributions (Poisson, Bernoulli and geometric).

Paper on A Generalized Stochastic Process For Count Data now published

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]

Too Big To Fail: Large Samples and the P-Value Problem -- forthcoming in ISR

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.

Subscribe to Modeling