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A note comparing component-slope, Scheffe and Cox parameterizations of the linear mixture experiment model

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  • Greg Piepel

Abstract

A mixture experiment involves combining two or more components in various proportions and collecting data on one or more responses. A linear mixture model may adequately represent the relationship between a response and mixture component proportions and be useful in screening the mixture components. The Scheffe and Cox parameterizations of the linear mixture model are commonly used for analyzing mixture experiment data. With the Scheffe parameterization, the fitted coefficient for a component is the predicted response at that pure component (i.e. single-component mixture). With the Cox parameterization, the fitted coefficient for a mixture component is the predicted difference in response at that pure component and at a pre-specified reference composition. This article presents a new component-slope parameterization, in which the fitted coefficient for a mixture component is the predicted slope of the linear response surface along the direction determined by that pure component and at a pre-specified reference composition. The component-slope, Scheffe, and Cox parameterizations of the linear mixture model are compared and their advantages and disadvantages are discussed.

Suggested Citation

  • Greg Piepel, 2006. "A note comparing component-slope, Scheffe and Cox parameterizations of the linear mixture experiment model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(4), pages 397-403.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:397-403
    DOI: 10.1080/02664760500449170
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