Using oversized models to find active variables in screening experiments

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(Project) M.Sc.
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Nonregular factorial designs can be used to conduct screening experiments involving many factors and their interactions, using a small number of runs. Linear model selection is challenging in this case because the design is not orthogonal, the number of potential models is huge, and the number of observations is small. A new procedure is proposed to aid model selection in such cases. A non-convergent simulated annealing algorithm is used to generate a large set of good models that are too big; common submodels within this set are then identified using visualization techniques. An automatic method of extracting the best smaller model from the oversized-model set is also proposed. The new method has good performance, and provides graphical output that can be very helpful in decision making. Although developed for industrial screening experiments, it can be applied to any suitable regression problem.
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