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On Relationship Between the AIC and the Overall Error Rates for Selection of Variables in a Discriminant Analysis

  • Yasunori Fujikoshi
Part of the Theory and Decision Library book series (TDLB, volume 8)

Summary

This paper deals with the problem of selecting the “best” subset of variables in a discriminant analysis with the aim of allocating future observations, in the context of two multivariate normal populations with the same covariance matrix. We consider the methods based on the following three criteria: (i) the AIC for the “no additional information” model, (ii) the overall error rate criterion based on the linear classification statistic and (iii) the overall error rate criterion based on the ML classification statistic. It is shown that there is a close relationship between the AIC and the overall error rate criteria.

Keywords

Error Rate Discriminant Analysis Asymptotic Expansion Asymptotic Distribution Classification Statistic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© D. Reidel Publishing Company, Dordrecht, Holland 1987

Authors and Affiliations

  • Yasunori Fujikoshi
    • 1
  1. 1.Department of Mathematics Faculty of ScienceHiroshima UniversityHiroshimaJapan

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