Hosted by the GPH Department of Biostatistics
Poisson regression is a popular tool for modeling count data and is applied in a vast array of disciplines. Real data, however, are often over- or under-dispersed relative to a Poisson model and, thus, are not conducive to Poisson regression. In this talk, Dr. Kimberly Sellers, Professor of Mathematics and Statistics at Georgetown University, introduces the Conway-Maxwell-Poisson (CMP) regression, which generalizes the well-known Poisson and logistic regressions, and is suitable for fitting count data with a wide range of dispersion levels.
About the Speaker:
Kimberly Sellers, PhD is a Professor of Mathematics and Statistics, specializing in Statistics at Georgetown University in Washington, DC; and a Principal Researcher with the Center for Statistical Research and Methodology Division of the U.S. Census Bureau. Her primary research interests and expertise center on statistical methods for count data that contain data dispersion with methodological interests in distribution theory, regression analysis, multivariate analysis, stochastic processes, and time series analysis. Sellers is an Elected Member of the International Statistical Institute (2018) and a Fellow in the American Statistical Association (2021) in recognition of her research, and active contributions to diversifying the fields of mathematical and statistical sciences with respect to gender and race/ethnicity. She was the 2017-2018 Chairperson for the American Statistical Association’s Committee on Women in Statistics, and currently serves as the inaugural chairperson of the ASA’s Justice, Equity, Diversity, and Inclusion (JEDI) Outreach Group, as well as an Advisory Board member for the Black Doctoral Network.