IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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Model-Robust G-Efficient Cuboidal Experimental Designs

입방형 영역에서의 G-효율이 높은 Model-Robust 실험설계

  • Park, You-Jin (Department of Business Administration, College of Social Sciences, Chung-Ang University) ;
  • Yi, Yoon-Ju (Department of Statistics, College of Natural Science, Chung-Ang University)
  • 박유진 (중앙대학교 상경학부 경영학과) ;
  • 이윤주 (중앙대학교 통계학과 대학원)
  • Received : 2009.10.24
  • Accepted : 2010.03.23
  • Published : 2010.06.01
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Abstract

The determination of a regression model is important in using statistical designs of experiments. Generally, the exact regression model is not known, and experimenters suppose that a certain model form will be fit. Then an experimental design suitable for that predetermined model form is selected and the experiment is conducted. However, the initially chosen regression model may not be correct, and this can result in undesirable statistical properties. We develop model-robust experimental designs that have stable prediction variance for a family of candidate regression models over a cuboidal region by using genetic algorithms and the desirability function method. We then compare the stability of prediction variance of model-robust experimental designs with those of the 3-level face centered cube. These model-robust experimental designs have moderately high G-efficiencies for all candidate models that the experimenter may potentially wish to fit, and outperform the cuboidal design for the second-order model. The G-efficiencies are provided for the model-robust experimental designs and the face centered cube.

Keywords

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