判别函数间关系的注解

Awogbemi, Clement Adeyeye
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引用次数: 0

摘要

本研究考虑了不同形式的判别函数及其出现的本质。还考虑了各种形式的分类问题,在上述每种情况下,都是从观测向量的简单函数而不是原始向量的高维空间中的复杂区域进行分类。违反线性判别函数(LDF)等方差协方差矩阵的条件,得到二次判别函数(QDF)。建立了所检验的分类统计量之间的关系:当两个样本量相等时,Anderson 's (W)和Rao 's (R)统计量相等,当一个常数等于1时,W、R和John-Kudo 's (Z)分类统计量渐近可比较。W和Z分类统计量之间也建立了线性关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Annotations on the Relationship Among Discriminant Functions
Different forms of discriminant functions and the essence of their appearances were considered in this study. Various forms of classification problems were also considered, and in each of the cases mentioned, classification from simple functions of the observational vector rather than complicated regions in the higher-dimensional space of the original vector were made. Violation of condition of equal variance covariance matrix for Linear Discriminant Function (LDF) results to Quadratic Discriminant Function (QDF). The relationships among the classification statistics examined were established: The Anderson’s (W) and Rao’s (R) statistics are equivalent when the two sample sizes are equal, and when a constant is equal to 1, W, R and John-Kudo’s (Z) classification statistics are asymptotically comparable. A linear relationship is also established between W and Z classification statistics.
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