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WIST and Statistics Seminar Series: Kimberly F. Sellers “A Flexible Regression Model for Dispersed Count Data”

When: Wednesday, April 28, 2021
11:00 AM - 12:00 PM Central

Where: Online

Audience: Faculty/Staff - Student - Post Docs/Docs - Graduate Students

Contact: Kisa Kowal   (847) 491-3974

Group: Department of Statistics

Category: Academic, Lectures & Meetings


Department of Statistics 2020-2021 Seminar Series (joint with Biostatistics), Co-hosted by Women in Statistics (WIST) - Spring 2021

“A Flexible Regression Model for Dispersed Count Data”

Kimberly F. Sellers, Professor, Department of Mathematics and Statistics, Georgetown University

While Poisson regression serves as a standard tool for modeling the association between a count response variable and explanatory variables, its underlying equi-dispersion assumption and its implications are well documented. The Conway-Maxwell-Poisson (COM-Poisson) distribution is a flexible count data alternative that allows for data over- or under-dispersion, thus the COM-Poisson regression can flexibly model associations involving a discrete count response variable and covariates. This talk introduces the resulting regression along with its zero-inflated analog, and the associated COMPoissonReg package in R which has become a popular resource for statistical computing.


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