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Abstract:
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Experiments exploring directions of steepest ascent (DSAs) are isomorphic to one-factor-at-a-time (1AAT) experiments - they try runs along the DSA at varying distances, typically with intermediate factor levels (IFLs). A natural extension of the 1AAT DSA approach likewise encourages IFLs, incorporates the DSA, and includes also the factor × DSA interactions. When combined with locally weighted scatterplot smoothing ("lowess"), this defines a class of varying-coefficient models called gamut models. Our main optimal design criterion is average ("integrated") mean square prediction error (I-optimality), which is relatively friendly to IFLs. We adapt this criterion to the lowess-based varying-coefficient gamut models and assess the resulting designs.
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