Optimal designs for both model discrimination and parameter estimation
AbstractThe KL-optimality criterion has been recently proposed to discriminate between any two statistical models. However, designs which are optimal for model discrimination may be inadequate for parameter estimation. In this paper, the DKL-optimality criterion is proposed which is useful for the dual problem of model discrimination and parameter estimation. An equivalence theorem and a stopping rule for the corresponding iterative algorithms are provided. A pharmacokinetics application is given to show the good properties of a DKL-optimum design.
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 InfoPaper provided by Universitá degli Studi di Milano in its series UNIMI - Research Papers in Economics, Business, and Statistics with number unimi-1071.
Date of creation: 05 May 2008
Date of revision:
Contact details of provider:
Postal: Via Conservatorio 7 - 20122 Milano
Phone: +39 02 503 16486
Fax: +39 02 503 16475
Web page: http://services.bepress.com/unimi
More information through EDIRC
D-optimality; T-optimality; Kullback-Leibler distance; KL-optimality;
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.