The design of model-robust experiments

Abstract

Model-robust designs are generated using a combination of algorithms based on several optimality criteria. The content of the candidate list and the sequencing of the point-selection algorithms has a major impact on overall design quality. When properly constructed, model-robust designs enable nonlinear variable effects to be visualized and estimated due to the sampling of the interior of the design space accomplished by these designs. There are also many cases in which the assumption of a normally and independently distributed (NID) experimental error is not valid. Model-robust designs are also shown to support these experiment circumstances, among others. Additionally, the model independence of these designs aids outlier and residuals diagnostics, and response data transformation analysis. Thus the many benefits of these designs more than offset the cost of the small number of additional runs required by these designs. Copyright © 2000 John Wiley & Sons, Ltd.