A Model Selection Procedure in Mixture-Process Experiments for Industrial Process Optimization

Abstract

ABSTRACT. We present a model selection procedure for use in Mixture and Mixture-Process Experiments. Certain combinations of restrictions on the proportions of the mixture components can result in a very constrained experimental region. This results in collinearity among the covariates of the model, which can make it difficult to fit the model using the traditional method based on the significance of the coefficients. For this reason, a model selection methodology based on information criteria will be proposed for process optimization. Two examples are presented to illustrate this model selection procedure.

A simplex dispersion model for improving precision in the odds ratio confidence interval in mixture experiments

“…Statistical modeling is done using polynomial models assuming normality for the response variable (Leão, Vieira, & Dal Bello, 2015). If response variables follows other known distributions, one can use generalized linear (mixed) models.…”