Balanced, nearly optimal mixture experiments for models with interactions with process variables
Prescott, Philip and Draper, Norman R. (2009) Balanced, nearly optimal mixture experiments for models with interactions with process variables. Quality Technology & Quantitative Management, 6, (2), 67-86.
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In mixture experiments, the settings of the predictor variables are limited by the fact that the ingredients are dependent. There may also be other, non-mixture, variables (process variables) to consider. Here, designs are considered for two types of second-order models in mixture and process variables, including certain mixture/process interactions. Several new balanced D-optimal (or nearly D-optimal) designs are proposed and are compared with quasi D-optimal designs and other balanced designs previously suggested. The properties of these new balanced designs are examined for the extended model with linear mixture by second-order process variable interactions; the designs are either D-optimal or nearly D-optimal and have diagonally partitioned information matrices, simplifying the model building process. An example from the bread industry illustrates the value of such designs.
|Keywords:||central composite designs, information matrices, mixture ingredient, process variables, response surface designs, simplex design, factorial design|
|Subjects:||Q Science > QA Mathematics
H Social Sciences > HA Statistics
|Divisions:||University Structure - Pre August 2011 > School of Mathematics > Statistics
|Date Deposited:||21 Aug 2008|
|Last Modified:||31 Mar 2016 12:35|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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