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A Compromise Experimental Design Method for Parametric Polynomial Response Surface Approximations

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  • Pradeep George
  • Madara Ogot

Abstract

This study presents a compromise approach to augmentation of experimental designs, necessitated by the expense of performing each experiment (computational or physical), that yields higher quality parametric polynomial response surface approximations than traditional augmentation. Based on the D-optimality criterion as a measure of experimental design quality, the method simultaneously considers several polynomial models during the experimental design, resulting in good quality designs for all models under consideration, as opposed to good quality designs only for lower-order models, as in the case of traditional augmentation. Several numerical examples and an engineering example are presented to illustrate the efficacy of the approach.

Suggested Citation

  • Pradeep George & Madara Ogot, 2006. "A Compromise Experimental Design Method for Parametric Polynomial Response Surface Approximations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1037-1050.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1037-1050
    DOI: 10.1080/02664760600746533
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    References listed on IDEAS

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    1. Raviprakash Salagame & Russell Barton, 1997. "Factorial hypercube designs for spatial correlation regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(4), pages 453-474.
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    Keywords

    Response surface method; surrogate models;

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