Optimal Designs for Quantile Regression Models
AbstractDespite 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.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of the American Statistical Association.
Volume (Year): 107 (2012)
Issue (Month): 499 (September)
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