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Bayesian optimal designs for discriminating between non-Normal models

Author

Listed:
  • Chiara Tommasi

    (University of Milano)

  • Jesus Lopez Fidalgo

    (University of Castilla La Mancha (Spain))

Abstract

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.

Suggested Citation

  • Chiara Tommasi & Jesus Lopez Fidalgo, 2007. "Bayesian optimal designs for discriminating between non-Normal models," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1055, Universitá degli Studi di Milano.
    • Handle: RePEc:bep:unimip:unimi-1055
    Note: oai:cdlib1:unimi-1055
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    Cited by:

    1. Jun Yu & HaiYing Wang, 2022. "Subdata selection algorithm for linear model discrimination," Statistical Papers, Springer, vol. 63(6), pages 1883-1906, December.

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