Balanced, nearly optimal mixture experiments for models with interactions with process variables
Balanced, nearly optimal mixture experiments for models with interactions with process variables
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.
central composite designs, information matrices, mixture ingredient, process variables, response surface designs, simplex design, factorial design
67-86
Prescott, Philip
cf0adfdd-989b-4f15-9e60-ef85eed817b2
Draper, Norman R.
49cd917e-5e95-4c51-bc04-667cc2c46ec2
2009
Prescott, Philip
cf0adfdd-989b-4f15-9e60-ef85eed817b2
Draper, Norman R.
49cd917e-5e95-4c51-bc04-667cc2c46ec2
Prescott, Philip and Draper, Norman R.
(2009)
Balanced, nearly optimal mixture experiments for models with interactions with process variables.
Quality Technology and Quantitative Management, 6 (2), .
Abstract
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.
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Published date: 2009
Keywords:
central composite designs, information matrices, mixture ingredient, process variables, response surface designs, simplex design, factorial design
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Statistics
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Local EPrints ID: 55643
URI: http://eprints.soton.ac.uk/id/eprint/55643
PURE UUID: cf12db9b-ceb1-42de-b726-9a6c4d29986d
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Date deposited: 21 Aug 2008
Last modified: 11 Dec 2021 17:45
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Author:
Norman R. Draper
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