Guest Lecturer: Professor William Li
Operations and Management Science Department
University of Minnesota

Presentation 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.