Screening designs for model selection

The problem of designing an experiment for selecting a good model from a set of models of interest is discussed in the setting where all factors have two levels. The models considered involve main effects and a few two-factor interactions. Two criteria for the selection of designs for model screening are introduced. One criterion selects designs that allow the maximum number of distinct models to be estimated (estimation capacity). The other maximizes the capability of the design to discriminate among competing models (model discrimination). Two-level orthogonal designs for 12, 16, and 20 runs that are optimal with respect to these criteria are constructed and tabulated for practical use. In addition, several approaches are discussed for the construction of nonorthogonal designs. The chapter includes new results on orthogonal designs that are effective for model discrimination.

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