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Optimal Designs for Quantile Regression Models


  • Holger Dette
  • Matthias Trampisch


Despite their importance, optimal designs for quantile regression models have not been developed so far. In this article, we investigate the D -optimal design problem for nonlinear quantile regression analysis. We provide a necessary condition to check the optimality of a given design and use it to determine bounds for the number of support points of locally D -optimal designs. The results are illustrated, determining locally, Bayesian and standardized maximin D -optimal designs for quantile regression analysis in the Michaelis--Menten and EMAX model, which are widely used in such important fields as toxicology, pharmacokinetics, and dose--response modeling.

Suggested Citation

  • Holger Dette & Matthias Trampisch, 2012. "Optimal Designs for Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1140-1151, September.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:499:p:1140-1151 DOI: 10.1080/01621459.2012.695665

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