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Optimal designs for both model discrimination and parameter estimation

Author

Listed:
  • Chiara Tommasi

    (University of Milano)

Abstract

The 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.

Suggested Citation

  • Chiara Tommasi, 2008. "Optimal designs for both model discrimination and parameter estimation," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1071, Universitá degli Studi di Milano.
    • Handle: RePEc:bep:unimip:unimi-1071
    Note: oai:cdlib1:unimi-1071
    as

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