Abstract
Experiments with mixture and process variables are often constructed as the cross product of a mixture and a factorial design. Often it is not possible to implement all the runs of the cross product design, or the cross product model is too large to be of practical interest.
We propose a methodology to select a model with a given number of terms and minimal condition number. The search methodology is based on weighted term orderings and can be extended to consider other statistical criteria.
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Maruri-Aguilar, H., Riccomagno, E. (2007). A Model Selection Algorithm for Mixture Experiments Including Process Variables. In: López-Fidalgo, J., Rodríguez-Díaz, J.M., Torsney, B. (eds) mODa 8 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-1952-6_14
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DOI: https://doi.org/10.1007/978-3-7908-1952-6_14
Publisher Name: Physica-Verlag HD
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