Box–Meyer method with Less Computational Complexity for 2-level Supersaturated Designs

抄録

A screening experiment focuses on narrowing down the factors that affect experimental results. To deal with the increased number of experimental runs due to an increase in the number of factors caused by product diversification, a screening experiment was applied to two-level supersaturated design.

When the design size becomes large, the computational complexity becomes enormous, and the analysis becomes impossible. Therefore, we propose a method for reducing the computational burden of the analysis and allowing it to be performed even for large experimental designs.

The Box–Meyer method calculates the posterior probabilities of all models and extracts factors all at once. The proposed method, meanwhile, extracts factors sequentially, adds them to the model, calculates the posterior probability and extracts factors again.

We compare the Box–Meyer method with the proposed method in simulations of seven different design sizes, which can be analyzed by the Box–Meyer method. Furthermore, we evaluate the proposed method in absolute terms in simulations of four different design sizes, that cannot be analyzed using the Box–Meyer method.

The simulation evaluation based on accuracy and running time, indices revealed that the proposed approach is better than the Box–Meyer method for large experimental designs.