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• W4365459553 abstract "When optimizing an experimental design for good prediction performance based on an assumed second order response surface model, it is common to focus on a single optimality criterion, either G-optimality, for best worst-case prediction precision, or I-optimality, for best average prediction precision. In this article, we illustrate how using particle swarm optimization to construct a Pareto front of non-dominated designs that balance these two criteria yields some highly desirable results. In most scenarios, there are designs that simultaneously perform well for both criteria. Seeing alternative designs that vary how they balance the performance of G- and I-efficiency provides experimenters with choices that allow selection of a better match for their study objectives. We provide an extensive repository of Pareto fronts with designs for 17 common experimental scenarios for 2 (design size N = 6 to 12), 3 (N = 10 to 16) and 4 (N = 15, 17, 20) experimental factors. These, when combined with a detailed strategy for how to efficiently analyze, assess, and select between alternatives, provide the reader with the tools to select the ideal design with a tailored balance between G- and I-optimality for their own experimental situations." @default.
• W4365459553 created "2023-04-15" @default.
• W4365459553 creator A5009541007 @default.
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• W4365459553 date "2023-04-13" @default.
• W4365459553 modified "2023-10-14" @default.
• W4365459553 title "<i>I</i>-optimal or <i>G</i>-optimal: Do we have to choose?" @default.
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• W4365459553 doi "https://doi.org/10.1080/08982112.2023.2194963" @default.
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