How to determine the optimal settings for D-Optimal Designs in Cornerstone?

Designed experiments in Cornerstone are often based on D-optimal designs which are flexible enough to handle special situations. The criteria for the design, namely the determinant of the information matrix after rescaling the factors to standard range fits well to the embedding theory and its reciprocal is often called generalized variance. In addition, a maximized determinant minimizes the coefficient confidence intervals. The construction of D-optimal designs relies on a iterative algorithm which uses a list of settings and conditions from the planned experiment under consideration.

This report describes how such settings can be chosen in an optimal way to get even better values for the mentioned generalized variance. The results concerning the algorithm are worked into Cornerstone 6.1. However, better settings for the framework parameters in the hand of the experimenter are also investigated to give recommendations in this field.

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