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.

Item Type: Article
ISSNs: 1684-3703 (print)
Related URLs:
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
ePrint ID: 55643
Accepted Date and Publication Date:
Date Deposited: 21 Aug 2008
Last Modified: 31 Mar 2016 12:35

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