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July 2004 An optimal discriminant rule in the class of linear and quadratic discriminant functions for large dimension and samples
Chieko Matsumoto
Hiroshima Math. J. 34(2): 231-250 (July 2004). DOI: 10.32917/hmj/1150998164

Abstract

For the classification problem between two normal populations with a common covariance matrix, we consider a class of discriminant rules based on a general discriminant function $T$. The class includes the one based on Fisher’s linear discriminant function and the likelihood ratio rule. Our main purpose is to derive an optimal discriminant rule by using an asymptotic expansion of misclassification probability when both the dimension and the sample sizes are large. We also derive an asymptotically unbiased estimator of the misclassification probability of $T$ in our class.

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Chieko Matsumoto. "An optimal discriminant rule in the class of linear and quadratic discriminant functions for large dimension and samples." Hiroshima Math. J. 34 (2) 231 - 250, July 2004. https://doi.org/10.32917/hmj/1150998164

Information

Published: July 2004
First available in Project Euclid: 22 June 2006

zbMATH: 1056.62078
MathSciNet: MR2086844
Digital Object Identifier: 10.32917/hmj/1150998164

Subjects:
Primary: 62H30

Rights: Copyright © 2004 Hiroshima University, Mathematics Program

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Vol.34 • No. 2 • July 2004
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