Truncated d-optimal designs for screening experiments

D-optimal designs have proved useful in analyzing common factorial experiments involving multilevel categorical factors. When analyzed by ANOVA, they allow the estimation of coefficients in a regression equation and the contributions to the variance by the main effects and interactions. If the measurement of contribution to variance is necessary but the estimation of all interaction coefficients in the regression equation is not, it is possible to reduce the number of experimental runs below a minimum D-optimal design, using what we call truncated D-optimal screening designs. D-efficiency calculations are not available due to the singularity of the design matrix; another method must be used to pare down the matrix while maintaining reasonable estimation of the original full factorial data. Covering arrays are adapted to guide this reduction. Combining properties of D-optimal designs and covering arrays produces designs that perform well at estimating full factorial results. A method is then developed to target specific interactions prior to the design of the experiment when process specific knowledge is available to indicate which interactions are least important.