Title:Order-of-Addition Modeling
Lecturer:Prof. Robert Mee，University of Tennessee
Time:2018.5.17,10:00-11:30 AM
Location:A1028
Invited by:Prof.Hui Bu
Abstract:
Since Van Nostrand (1995), the literature on order-of-addition studies has generally relied on main effects models constructed from pair-wise ordering (PWO) factors. This talk reviews that literature and proposes an extension to Van Nostrand’s model by adding interactions of PWO factors to explain variation that is best accounted for by the ordering of sets of three or more components. An example is given where five cancer drugs were applied to tissue samples in 40 different sequences. That is, 40 of the possible 5! = 120 sequences were studied in a lab setting. From these 40, we build a model to predict which of the 120 possible sequences would perform best. The talk includes how to select subsets of sequences to efficiently estimate order-of-addition models. The talk will conclude by asking for suggestions of order-of-addition applications in business.
About the presenter:
Dr. Robert Mee is the William and Sara Clark Professor of Business Analytics in the Haslam College of Business at the University of Tennessee. He teaches statistical methods for the Haslam College’s award-winning Business Analytics masters program. He is Director for the Haslam College Executive in Residence Program. He is also a part-time Lecture Professor at Nankai University’s Institute of Statistics in Tianjin, China, through the Tianjin Regional Thousand Talents program.
Dr. Mee received his B.S. in Management Science from Georgia Institute of Technology, and his M.S. and Ph.D. in Statistics from Iowa State University. Dr. Mee is an elected fellow of the American Statistical Association and has authored more than 50 refereed journal articles. He served on Technometrics’ Management Committee for 12 years and as an Associate Editor for 7 years. Currently he serves on the Journal of Quality Technology’s Editorial Board.
Dr. Mee’s research interests include design and analysis of experiments, conjoint analysis, and customer analytics. He is the author of A Comprehensive Guide to Factorial Two-Level Experimentation (Springer 2009).
