Augmenting supersaturated designs with Bayesian D-optimality

作者：Gutman Alex J^*; White Edward D; Lin Dennis K J; Hill Raymond R

来源：Computational Statistics & Data Analysis, 2014, 71: 1147-1158.

DOI：10.1016/j.csda.2013.09.009

• Adding runs
• Augmentation
• Computer-generated designs
• Experimental design
• Screening designs
• Supersaturated designs

A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D-optimality criterion to increase the information gained about those factors. Simulation results show the method can outperform existing supersaturated design augmentation strategies that add runs without analyzing the initial response variables.

• 出版日期2014-3
• 单位中国人民解放军空军电子技术研究所