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Optimal prediction variance properties of some central composite designs in the hypercube

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  • Charity Uchenna Onwuamaeze

Abstract

Measures of the prediction variance performances of three variations of central composite designs when the region is hypercube were examined and compared using the G-efficiency and I-optimality criterion so as to determine the economical design(s) that perform(s) better than the other. The hypercube is the multidimensional cuboidal region with the axial distance, α=1.0. The designs are the standard central composite designs (CCD), the small composite design (SCD) and minimum run resolution V (minResV) design. Two prediction variance based optimality criteria, I-optimality and G-efficiency were determined, a plot of variance dispersion graph and fraction of design space are used to give a comprehensive picture of the behavior of the prediction variance throughout the region of interest. Comparing the three designs mentioned above for 3, 4, and 5 factors on cuboidal region, the result showed that CCD performed better than SCD and MinResVdesign considering 2, 3, and 5 center points.

Suggested Citation

  • Charity Uchenna Onwuamaeze, 2021. "Optimal prediction variance properties of some central composite designs in the hypercube," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(8), pages 1911-1924, April.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:8:p:1911-1924
    DOI: 10.1080/03610926.2019.1656746
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