An optimal discriminant rule in the class of linear and quadratic discriminant functions for large dimension and samples

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概要

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• For the classi?cation 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 misclassi?cation probability when both the dimension and the sample sizes are large. We also derive an asymptotically unbiased estimator of the misclassi?cation probability of $T$ in our class.

• 広島大学の論文

著者

• Matsumoto Chieko

  Department Of Mathematics Graduate School Of Science Hiroshima University

関連論文

• Asymptotic expansion of the null distribution of the modified normal likelihoodratio criterion for testing $\Sigma=\Sigma_0$ under nonnormality
• An optimal discriminant rule in the class of linear and quadratic discriminant functions for large dimension and samples

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