Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data

By M. Peruggia. 2003

685 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cd63b4a19e9bcd3b56965f6dde6f5288e807fc21

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Citations in the corpus

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, methodology): “In RVEA, the canonical simplex-lattice design method [21] is used to generate a set of uniformly distributed reference vectors in the objective space.”
Six Sigma Black Belts: What Do They Need to Know? (2001, background): “…Industry Action Group (1995) General DOE – Box, Hunter, and Hunter (1978); Montgomery (1991) General Statistics – Walpole and Myers (1993) Measurement Systems Analysis – Wheeler and Lyday (1990); Automotive Industry Action Group (1990) Mixture Designs – Cornell (1990) Quality Function…”
Applied Predictive Modeling (2013, methodology): “Specific experimental designs (and linear model forms) exist for experiments that combine mixture and process variables (see Cornell (2002) for more details).”
I-Optimal Design of Mixture Experiments (2016, methodology): “This model adds specific higher-order terms to the special cubic model, but it is not commonly used (see, e.g., Cornell 2002; Smith 2005).”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “First, a set of uniformly distributed points on a unit hyperplane are generated using the canonical simplex-lattice design method [65] { ui = ( u(1)i , u 2 i , .”
A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, methodology): “For an m-objective space, a group of K uniformly distributed points on a unit hyperplane is generated using the canonical simplex-lattice design method [57] { wk ∈ { 0 H , 1 H , .”
Regression Analysis for Microbiome Compositional Data (2016, methodology): “Such data are also referred to as mixture data [Aitchison and Bacon-Shone (1984), Cornell (2002), Snee (1973)].”
A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization (2018, methodology): “5) Number of reference vectors (N): number of reference vectors is determined by the design factors for simplexlattice design [11] and the number of objectives and”