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Bayesian optimal designs for discriminating between non-Normal models

  • Chiara Tommasi

    (University of Milano)

  • Jesus Lopez Fidalgo

    (University of Castilla La Mancha (Spain))

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    Designs are found for discriminating between two non-Normal models in the presence of prior information. The KL-optimality criterion, where the true model is assumed to be completely known, is extended to a criterion where prior distributions of the parameters and a prior probability of each model to be true are assumed. Concavity of this criterion is proved. Thus, the results of optimal design theory apply in this context and optimal designs can be constructed and checked by the General Equivalence Theorem. Some illustrative examples are provided.

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    Paper provided by Universit√° degli Studi di Milano in its series UNIMI - Research Papers in Economics, Business, and Statistics with number unimi-1055.

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    Date of creation: 08 May 2007
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    Handle: RePEc:bep:unimip:unimi-1055
    Note: oai:cdlib1:unimi-1055
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