Скачать или смотреть Kimberly Sellers - Analyzing Count Data Expressing Data Dispersion

Analyzing Count Data Expressing Data Dispersion by Kimberly Sellers. Visit rstats.ai/gov/ to learn more.

Abstract:
It is natural to consider a Poisson model to analyze count data, however such approaches maintain a constraining underlying equi-dispersion assumption (i.e. that the (conditional, when applicable) mean and variance equal); this assumption can lead to spurious results and inferences. Instead, much work has been conducted developing flexible alternative methods stemming from the Conway-Maxwell-Poisson (CMP) distribution -- a two parameter distribution for count data that contains the Poisson model (among others) as a special case. To illustrate the impact of such contributions, this talk focuses on CMP regression models and related R packages available to perform such analyses.

Bio:
Kimberly F. Sellers, Ph.D. 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 research areas of interest and expertise are in generalized statistical methods involving count data that contain data dispersion; and in image analysis techniques, particularly low-level analyses including preprocessing, normalization, feature detection, and alignment. She is a frequently requested speaker (both nationally and internationally) for her research and/or service activities regarding increasing diversity in the STEM (Science, Technology, Engineering, Math) fields, particularly in Statistics, with regard to race/ethnicity and/or gender.

Twitter: @KimFlaggSellers

Presented at the inaugural Government & Public Sector R Conference | New York (December 4, 2020)