Techniques for the Analysis of Mixture Data

Ronald D. Snee

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Optimal Experimental Design Methods Statistical Methods and Applications Bayesian Methods and Mixture Models

Abstract

Techniques for the analysis of mixture data are discussed. It is shown that mixture models often can be simplified by observing near equalities among the estimated coefficients. Expressing the components in terms of pseudocomponents will sometimes improve the fit of Becker's models [1] and improve the numerical analysis. Scatter plots are useful in detecting dominant components in a mixture system. The ratio model is shown to be a useful alternative to the polynomial and Becker's models. Examples are presented to illustrate these methods of analysis.

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References

1951On the Experimental Attainment of Optimum Conditions 1992On the Experimental Attainment of Optimum Conditions 1958Experiments with Mixtures 1963The Simplex-Centroid Design for Experiments with Mixtures 1966Extreme Vertices Design of Mixture Experiments 1962Simplex Lattice Designs for Multicomponent Systems 1968Models for the Response of a Mixture 1955Use of the Simplex Design in the Study of Joint Action of Related Hormones

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2010Do Instructional Attributes pose Multicollinearity Problems? An Empirical Exploration 1981Developing Blending Models for Gasoline and Other Mixtures 1985Models for Mixture Experiments When the Response Depends on the Total Amount 1982Measuring Component Effects in Constrained Mixture Experiments 2000Optimal designs for experiments with mixtures: a survey 1978MODELS AND DESIGNS FOR EXPERIMENTS WITH MIXTURES

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