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R-optimal designs for multi-response regression models with multi-factors

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  • Pengqi Liu
  • Lucy L. Gao
  • Julie Zhou

Abstract

We investigate R-optimal designs for multi-response regression models with multi-factors, where the random errors in these models are correlated. Several theoretical results are derived for R-optimal designs, including scale invariance, reflection symmetry, line and plane symmetry, and dependence on the covariance matrix of the errors. All the results can be applied to linear and non-linear models. In addition, an efficient algorithm based on an interior point method is developed for finding R-optimal designs on discrete design spaces. The algorithm is very flexible, and can be applied to any multi-response regression model.

Suggested Citation

  • Pengqi Liu & Lucy L. Gao & Julie Zhou, 2022. "R-optimal designs for multi-response regression models with multi-factors," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(2), pages 340-355, January.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:2:p:340-355
    DOI: 10.1080/03610926.2020.1748655
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