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Comparing the Slack-Variable Mixture Model With Other Alternatives

Summary: [This article is based on the authors' abstract.] There have been many linear regression models proposed to analyze mixture experiments including the Scheffe model, the slack-variable model, and the Kronecker model. The use of the slack-variable model is somewhat controversial within the mixture experiment research community. However, in situations that the slack-variable ingredient is used to fill in the formulation and the remaining ingredients have constraints such that they can be chosen independently of one another, the slack-variable model is extremely popular by practitioners mainly due to the ease of interpretation. In this article, we advocate that for some mixture experiments the slack-variable model has appealing properties including numerical stability and better prediction accuracy when model-term selection is performed. We also explain how the effects of the slack-variable model components should be interpreted and how easy it is for practitioners to understand the components effects. We also investigate how to choose the slack-variable component, what transformation should be used to reduce collinearity, and under what circumstances the slack-variable model should be preferred. Both simulation and practical examples are provided to support the conclusions.

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  • Topics: Statistics
  • Keywords: Mixture experiments, Mixture variables, Uncertainty, Optimization, Proportion
  • Author: Kang, Lulu; Salgado, Javier Cruz; Brenneman, William A.
  • Journal: Technometrics