Speaker: Christopher Franck, Ph.D., Associate Professor, Department of Statistics, Virginia Tech
Title: An Introduction to Model Uncertainty and Averaging for Categorical Data Analysis
Date: Tuesday, June 21st
Time: 2:00 PM – 3:30 PM Eastern / 11:00 AM – 12:30 PM Pacific
Registration (Zoom, free): SDNS Webinar Registration
Abstract: Categorical data analysis is ubiquitous in the 21st century, and its analysis is vital to advance research in many domains. In an era with ever-expanding availability of data, the choice of which statistical model should be used is as important as ever. While statistical techniques to choose among competing models have been commonplace for a while, it seems that accessible techniques to effectively combine inferences over competitive models are not as widely used in practice. The purpose of this short course is to describe techniques that enable researchers to simultaneously leverage a variety of candidate models to improve prediction and inference. We describe an easy-to-use technique (based on the Bayesian information Criterion) to conduct approximate Bayesian model averaging, which weighs inferences proportionally to each candidate models' posterior probability and can provide improved out-of-sample predictive performance over an individual model. We also discuss stacking, which combines model predictions according to each models' out-of-sample predictive capability. Basic familiarity with logistic regression is a prerequisite for this course.
About the Speaker: Christopher Franck is an Associate Professor in the Department of Statistics at Virginia Tech. His research focuses on Bayesian model selection and averaging, objective Bayes, and spatial statistics with specific emphasis in health applications. He previously served as the Assistant Director of the Laboratory for Interdisciplinary Statistical Analysis (LISA) where he worked on a variety of research projects spanning the medical, psychological, bioinformatic, biomechanical fields.Sponsored by the Section on Statistics in Defense and National Security (SDNS). For more information about the SDNS webinar series, please visit the ASA SDNS website.