I-Optimal Versus D-Optimal Split-Plot Response Surface Designs

Journal of Quality Technology vol. 44 issue 2 - April 2012

Abstract: While response surface experiments should ideally focus on predictive capability, the most common criterion for creating optimal response surface designs is D-optimality, which minimizes the variance of all factor effect estimates collectively. I-optimal designs minimize the average variance of prediction over the experimental region, making them more appropriate than D-optimality for response surface designs. This article discusses I-optimal design for split-plot response surface experiments. Several examples show these designs to have improved prediction over D-optimal split-plot designs and to produce very precise factor effect estimates.

Keywords: Algorithm; D-optimality; Estimation; Prediction; Response surface methodology (RSM); Split-plot design; Variance (statistics)

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