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An optimal strategy for sequential classification on partially ordered sets

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  • S. Ferguson, T.Thomas
  • Tatsuoka, Curtis
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    Abstract

    A decision-theoretic framework is described for sequential classification when the parameter space is a finite partially ordered set. An example of an optimal strategy is then presented. This example establishes that an asymptotically optimal class of experiment selection rules is not necessarily optimal in the given decision-theoretic setting.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 68 (2004)
    Issue (Month): 2 (June)
    Pages: 161-168

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    Handle: RePEc:eee:stapro:v:68:y:2004:i:2:p:161-168

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    Related research

    Keywords: Sequential selection of experiment Group testing Cognitively diagnostic educational testing Decision theory;

    References

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    1. Curtis Tatsuoka & Thomas Ferguson, 2003. "Sequential classification on partially ordered sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 143-157.
    2. Curtis Tatsuoka, 2002. "Data analytic methods for latent partially ordered classification models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 337-350.
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