Model-Robust and Model-Discriminating Designs

GUEST LECTURER
William Li

AFFILIATION
University of Minnesota

ABSTRACT
We discuss the problem of designing an experiment for selecting
a good model from a set of models of interest. The research is built on
the work on model-robust design of Li and Nachtsheim (2000) and
model-discriminating designs of Jones, Li, Nachtsheim, and Ye (2007,
2008). We introduce new criteria for model discrimination and use these
and existing criteria to evaluate standard orthogonal designs. We also use
these criteria to construct optimal two-level designs for screening
experiments. Results indicate that, for a given sample size and number of
desired factors, not all orthogonal designs are model-discriminating
designs for the model spaces considered. We conclude with some simulation
studies, which show that the proposed designs can lead to a higher
probability of identifying the correct model in the data analysis
procedure than traditional minimum aberration designs.

DATE & TIME
Tuesday, February 26, 2008 -- 11:00 AM

DURATION
1 hour

LOCATION
Executive Classroom