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T-optimality: a stopping rule for a first order algorithm

Author

Listed:
  • Jesus LOPEZ FIDALGO
  • Chiara TOMMASI
  • Paula Camelia TRANDAFIR

Abstract

In the optimal design theory, the T-optimality criterion is useful for the discrimination between two competitive models. This criterion has an interesting statistical interpretation as the power of a test for the fit of a second model when the first one is true. Usually there is not a closed form for the T-optimum design and it must be computed through an iterative procedure. In this short note a stopping rule for a first order algorithm is provided. The proposed stopping rule has the interesting feature that the algorithm will always stop at a design which reaches a minimum fixed efficiency. In other words, the algorithm stops when it reaches a design efficiency (with respect to the unknown T-optimum design) as good as wanted.

Suggested Citation

  • Jesus LOPEZ FIDALGO & Chiara TOMMASI & Paula Camelia TRANDAFIR, 2004. "T-optimality: a stopping rule for a first order algorithm," Departmental Working Papers 2004-30, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    • Handle: RePEc:mil:wpdepa:2004-30
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    File URL: http://wp.demm.unimi.it/files/wp/2004/DEMM-2004_030wp.pdf
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    Cited by:

    1. J. López‐Fidalgo & C. Tommasi & P. C. Trandafir, 2007. "An optimal experimental design criterion for discriminating between non‐normal models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 231-242, April.

    More about this item

    Keywords

    optimal design; discrimination models;

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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