Optimal designs for both model discrimination and parameter estimation
Chiara Tommasi, University of Milano
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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.
SUBJECT AREA:
C9
SUGGESTED CITATION:
Chiara Tommasi,
"Optimal designs for both model discrimination and parameter estimation"
(May 2008).
UNIMI - Research Papers in Economics, Business, and Statistics.
Statistics and Mathematics.
Working Paper 34.
http://services.bepress.com/unimi/statistics/art34
Paper presented by S.M. Iacus.
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