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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of the American Statistical Association.
Volume (Year): 107 (2012)
Issue (Month): 499 (September)
Contact details of provider:
Web page: http://www.tandfonline.com/UASA20
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.