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Classification by fiducial predictive density functions

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  • Yuqi Long
  • Xingzhong Xu

Abstract

For a classification problem with given loss function, Bayesian methods lead to the minimal risk among all possible classifiers. However, the prior distribution is often selected with some subjective thoughts. Motivated by this, we consider to replace the posterior distribution by a fiducial distribution, and use the fiducial predictive density to substitute the true but unknown underlying density functions to construct a new classification rule. The newly obtained classifier was proved to have oracle property. Simulations also show that the new classification rule performs better than traditional classifiers.

Suggested Citation

  • Yuqi Long & Xingzhong Xu, 2022. "Classification by fiducial predictive density functions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(15), pages 5187-5203, June.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:15:p:5187-5203
    DOI: 10.1080/03610926.2020.1836218
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