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Optimal design of experiments for non‐linear response surface models

Author

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  • Yuanzhi Huang
  • Steven G. Gilmour
  • Kalliopi Mylona
  • Peter Goos

Abstract

Many chemical and biological experiments involve multiple treatment factors and often it is convenient to fit a non‐linear model in these factors. This non‐linear model can be mechanistic, empirical or a hybrid of the two. Motivated by experiments in chemical engineering, we focus on D‐optimal designs for multifactor non‐linear response surfaces in general. To find and study optimal designs, we first implement conventional point and co‐ordinate exchange algorithms. Next, we develop a novel multiphase optimization method to construct D‐optimal designs with improved properties. The benefits of this method are demonstrated by application to two experiments involving non‐linear regression models. The designs obtained are shown to be considerably more informative than designs obtained by using traditional design optimality algorithms.

Suggested Citation

  • Yuanzhi Huang & Steven G. Gilmour & Kalliopi Mylona & Peter Goos, 2019. "Optimal design of experiments for non‐linear response surface models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 623-640, April.
  • Handle: RePEc:bla:jorssc:v:68:y:2019:i:3:p:623-640
    DOI: 10.1111/rssc.12313
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    Cited by:

    1. Yuanzhi Huang & Steven G. Gilmour & Kalliopi Mylona & Peter Goos, 2020. "Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 601-616, December.
    2. García-Ródenas, Ricardo & García-García, José Carlos & López-Fidalgo, Jesús & Martín-Baos, José Ángel & Wong, Weng Kee, 2020. "A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    3. Ul Hassan, Mahmood & Miller, Frank, 2021. "An exchange algorithm for optimal calibration of items in computerized achievement tests," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

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