In fitting empirical models to experimental data, bias due to model inaccuracy is often a serious problem. A particular case in point is computer experiments, in which there is no random error, only model error. Further, it may be that just a few of the experimental factors account for most of the variation in the response. In such settings, it may be desirable to use a design that is able, simultaneously, to screen out the important factors and to fit a higher-order model in those factors. We derive a class of such designs by rotating standard two-level fractional factorials. Special classes of rotations are developed to achieve some appealing symmetries in the design. A comparison of designs based on their alias matrices shows that the new designs are better than many other alternatives.

This is joint work with Dizza Bursztyn.