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Wednesday, August 07, 2019
10:00 AM - 11:00 AM
CNLS Conference Room (TA-3, Bldg 1690)

Seminar

Flexible Regression Models for Dispersed Count Data

Kimberly Sellers
Georgetown University

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications across disciplines. Real data, however, are often over- or under-dispersed relative to the Poisson model, and thus are not conducive to Poisson regression. This talk presents a regression model based on the Conway-Maxwell-Poisson (COM-Poisson or CMP) distribution which serves as a flexible alternative that contains both the Poisson and logistic regressions as special cases, and can handle other count data with a range of dispersion levels. We discuss model estimation, inference, diagnostics etc. for both the standard CMP regression and its zero-inflated analog, and introduce the associated R package, COMPoissonReg, developed to aid analysts with such data.

To meet with the speaker: Contact Emily Casleton, ecasleton@lanl.gov

Upcoming invited seminars:
8/14: Laura Freeman, Virginia Tech
8/21: Mark Glickman, Harvard University
8/28: Trisalyn Nelson, Arizona State University
9/12: David van Dyk, Imperial College London

Host: Invited Statistical Sciences Seminar Series (CCS-6)