Speaker
Dr. Jon Stallrich
Associate Professor of Statistics
North Carolina State University
Date
Friday, April 18, 2025
11:00 A.M. – 12:00 P.M. ET
Location
Jajodia Auditorium, Room 1101
Nguyen Engineering Building
4511 Patriot Circle
Fairfax, Virginia 22030
Optimal Designs for Two-Stage Inference
Abstract
The analysis of screening experiments is often done in two stages, starting with factor selection via an analysis under a main effects model. The success of the first stage is influenced by three components: (1) main effect estimators’ variances and (2) bias, and (3) the estimate of the noise variance. Component (3) has only recently been given attention with design techniques that ensure an unbiased estimate of the noise variance. In this talk, I propose a design criterion based on expected confidence intervals of the first stage analysis that naturally balances all three components. To address model misspecification, I propose a constrained design criterion that measures the inflation of common model selection criteria for underspecified models. A general computer search algorithm is presented and a direct construction method is also proposed for common screening models.
About the Speaker
Jon Stallrich is an Associate Professor in the Department of Statistics at North Carolina State University. He earned his Ph.D. in Statistics from Virginia Tech in 2014. His research interests include design and analysis of screening experiments, computer experiments, online controlled experiments, functional data analysis, and variable selection.
Event Organizer
David Kepplinger
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University