A new type of robust designs for chemometrics and computer experiments

Kai Tai Fang*, Yuxuan Lin, Heng Peng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

There are many difficulties that may be encountered in experiments of chemometrics as well as most computer experiments, that ought to explore an approximate model instead of the true one that is complicated. Space-filling designs including uniform designs are robust for such situation. However, when the underlying regression model is known, the D-optimal design (DOD) is the most effective on parameter estimation, but DOD is not robust against the model change. In this paper, we propose a new type of composite designs guaranteeing both robustness and effectiveness. Subsequently, we compare the prediction performance of the seven candidate composite designs, under various case studies. For the convenience of implementation, instead of chemical experiments, we adopt computer experiments as illustration involving some popular models that have been widely applied in evaluating the performance of optimization algorithms. Among all candidate composite designs, we recommend two of them based on their advantaged performance in all cases we explored. Our recommendation is also suitable for most physical experiments.

Original languageEnglish
Article number104474
JournalChemometrics and Intelligent Laboratory Systems
Volume221
Early online date4 Dec 2021
DOIs
Publication statusPublished - 15 Feb 2022

Scopus Subject Areas

  • Analytical Chemistry
  • Software
  • Computer Science Applications
  • Process Chemistry and Technology
  • Spectroscopy

User-Defined Keywords

  • Chemometrics
  • D-optimal design
  • Kriging model
  • Orthogonal design
  • Uniform design

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