Rethinking Uncertainty Quantification By Prof. Edward Boone
Model Validation is often a difficult task with many different objectives and their associated metrics. Many times these metrics can address a single issue such as model fit, predictive performance, uncertainty quantification, etc. One quantity of interest is the amount of uncertainty associated with model misspecification. In this talk we present a bootstrap based method for uncertainty quantification under model misspecification.
Overview
Abstract
Model Validation is often a difficult task with many different objectives and their associated metrics. Many times these metrics can address a single issue such as model fit, predictive performance, uncertainty quantification, etc. One quantity of interest is the amount of uncertainty associated with model misspecification. In this talk we present a bootstrap based method for uncertainty quantification under model misspecification. We explore the method via a simulation study. These are illustrated using a spring elasticity dataset.
Brief Biography
Edward L. Boone is an Associate Professor of Statistics in the Department of Statistical Science and Operations Research at Virginia Commonwealth University in Richmond Virginia. He earned his BS degree from Bowling Green State University, an MS in Mathematics from Miami University, an MS and PhD in Statistics from Virginia Tech. His research interests are in applied Bayesian methodology for environmental and health applications and uncertainty quantification for dynamical systems.
Refreshments: Available in 4214 @ 2:45 pm
