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An optimal strategy for sequential classification on partially ordered sets

Author

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

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.

Suggested Citation

  • S. Ferguson, T.Thomas & Tatsuoka, Curtis, 2004. "An optimal strategy for sequential classification on partially ordered sets," Statistics & Probability Letters, Elsevier, vol. 68(2), pages 161-168, June.
  • Handle: RePEc:eee:stapro:v:68:y:2004:i:2:p:161-168
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    References listed on IDEAS

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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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