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
Abstract
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.
Kisa Kowal
(847) 491-3974
Email