Screening is the process of sifting through a set of factors via experimentation to determine the few important factors that have a substantial effect on a response. When the set of factors is large and interactions are anticipated, screening methods using single fractional factorial designs may require too many observations to be feasible, and the methodology of two-stage group screening has b...

When fitting a linear regression model to data, the effects not included in the model can confound those included in the model, resulting in incorrect estimates of the regression coefficients and incorrect inferences as to whether a term is significant. This paper shows how uniform designs can reduce this aliasing. The discrepancy is a quantitative measure of how uniformly design points are pla...

SUMMARY We propose a general and unified approach to the selection of regular fractional factorial designs which can be applied to experiments that are unblocked, blocked, with random or fixed block effects, or have a split-plot structure. Our criterion is derived as a good surrogate for the model-robustness criterion of information capacity. In the case of random block effects, it takes the ra...

Minimum aberration is a popular criterion for selecting fractional factorial designs. It aims at the minimization of aliasing among lower-order effects. Cheng, Steinberg and Sun (1999) showed that it is a good surrogate for maximum estimation capacity, a model robustness criterion, but they are not the same, especially for resolution IV designs. In this paper, the relationship between these two...

Metamodels are effective for providing fast-running surrogate approximations of product or system performance. Since these approximations are generally based on continuous functions, they can provide poor fits of discontinuous response functions. Many engineering models produce functions that are only piecewise continuous, due to changes in modes of behavior or other state variables. In this pa...