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Classification using semiparametric mixtures

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  • Yong Wang
  • Xuxu Wang

Abstract

A new density-based classification method that uses semiparametric mixtures is proposed. Like other density-based classifiers, it first estimates the probability density function for the observations in each class, with a semiparametric mixture, and then classifies a new observation by the highest posterior probability. By making a proper use of a multivariate nonparametric density estimator that has been developed recently, it is able to produce adaptively smooth and complicated decision boundaries in a high-dimensional space and can thus work well in such cases. Issues specific to classification are studied and discussed. Numerical studies using simulated and real-world data show that the new classifier performs very well as compared with other commonly used classification methods.

Suggested Citation

  • Yong Wang & Xuxu Wang, 2019. "Classification using semiparametric mixtures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(11), pages 2056-2074, August.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:2056-2074
    DOI: 10.1080/02664763.2019.1579306
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