title: General Blending Models for Data From Mixture Experiments.
abstract: We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online.
authors.affiliation: University of Manchester , Oxford Road, Manchester , M13 9PL , UK ( Liam.Brown@manchester.ac.uk ; A.N.Donev@manchester.ac.uk ).
authors.forename: L
authors.initials: L
authors.lastname: Brown
authors.affiliation: University of Manchester , Oxford Road, Manchester , M13 9PL , UK ( Liam.Brown@manchester.ac.uk ; A.N.Donev@manchester.ac.uk ).
authors.forename: A N
authors.initials: AN
authors.lastname: Donev
authors.affiliation: Federal-Mogul Friction Products Ltd , High Peak, Derbyshire , SK23 0JP , UK ( Alastair.Bissett@federalmogul.com ).
authors.forename: A C
authors.initials: AC
authors.lastname: Bissett
keywords: Becker’s models; Model selection; Nonlinear models; Scheffé polynomials.