Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Discriminant Analysis of Binary Data with Multinomial Distribution by Using the Iterative Cross Entropy Minimization Estimation

  • Lee Jung Jin (Department of Statistics, Soong Sil University)
  • Published : 2005.04.01
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Abstract

Many discriminant analysis models for binary data have been used in real applications, but none of the classification models dominates in all varying circumstances(Asparoukhov & Krzanowski(2001)). Lee and Hwang (2003) proposed a new classification model by using multinomial distribution with the maximum entropy estimation method. The model showed some promising results in case of small number of variables, but its performance was not satisfactory for large number of variables. This paper explores to use the iterative cross entropy minimization estimation method in replace of the maximum entropy estimation. Simulation experiments show that this method can compete with other well known existing classification models.

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References

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Cited by

  1. Discriminant analysis of binary data following multivariate Bernoulli distribution vol.38, pp.6, 2011, https://doi.org/10.1016/j.eswa.2010.12.126