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.
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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
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D-optimality; T-optimality; Kullback-Leibler distance; KL-optimality;
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