Fraction of Design Space Plots for Examining Model Robustness

Abstract

When choosing between competing designs, it is typical to specify a design space and model on which to base the comparison. The prediction capabilities of the design, specifically G- and V-efficiency using scaled prediction variance (SPV), are based on this chosen model. After the data are collected and individual effects are tested, some terms may not be significant in the model. In this case, the experimenter likely will decide to use a reduced model, which has only a portion of the terms included that were in the original model for which the design was chosen. This paper presents a graphical method for examining design robustness related to the SPV values using fraction of design space (FDS) plots by comparing designs across a number of potential models in a prespecified model space. The FDS plots show the various distributions of the SPV throughout the design space for different models for a chosen design on the same graph. The methods are demonstrated on several examples for different models and on a variety of design spaces.

• Alphabetical Optimality Criteria
• FDS Plot
• Mixture Experiments
• Model Reduction
• Response Surface Designs
• Scaled Prediction Variance

Additional information

Notes on contributors

Ayca Ozol-Godfrey

Dr. Ozol-Godfrey is a Senior Biostatistician at Wyeth Vaccines Research. She is a member of ASA. Her e-mail address is [email protected].

Christine M. Anderson-Cook

Dr. Anderson-Cook is a Research Scientist in the Statistical Sciences Group at Los Alamos National Laboratory. She is a Senior Member of ASQ. Her e-mail address is [email protected].

Douglas C. Montgomery

Dr. Montgomery is a Professor in the Department of Industrial Engineering. He is a Fellow of ASQ. His e-mail address is [email protected].