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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))


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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    References listed on IDEAS

    1. Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
    2. Nikita Ratanov, 2005. "Quantil Hedging for telegraph markets and its applications to a pricing of equity-linked life insurance contracts," BORRADORES DE INVESTIGACIÓN 003410, UNIVERSIDAD DEL ROSARIO.
    3. Nikita Ratanov, 2007. "A jump telegraph model for option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 7(5), pages 575-583.
    4. Chen, Gongmeng & Choi, Yoon K. & Zhou, Yong, 2005. "Nonparametric estimation of structural change points in volatility models for time series," Journal of Econometrics, Elsevier, vol. 126(1), pages 79-114, May.
    5. Mazza, Christian & Rulliere, Didier, 2004. "A link between wave governed random motions and ruin processes," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 205-222, October.
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