Abstract
In this article, a robust regression technique called MM-estimator is proposed to model the responses in response surface methodology (RSM). Model fitting based on the MM-estimator allows practitioners to find a better optimal setting in dual-response surface optimization and multiple-response optimization when the responses are considerably nonnormal and/or contain some outliers. The MM-estimator is one of the robust regression techniques that can dampen the effect of the outliers. An example from the Roman catapult experiment is used to illustrate our proposal.
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