| Abstract: | A new robust criterion for experimental designs is proposed. For a given design, the criterion is to minimize, over all possible run orders, the absolute value of the change of the variance function due to possible correlation between the observations. The resulting design is robust against possible auto-correlation among the observations in the sense that confidence-interval coverage levels are maintained accurately. Applications are given for 2k factorial designs and one mixed-level fractional experiment, and robust runs are obtained. Computational strategies are also discussed, and a simulated annealing algorithm is developed to search for robust designs. |
