THEORETICAL DISTRIBUTIONS OF OPTIMA FOR UNIVARIATE DISCRIMINATION OF RANDOM DATA

被引：62

作者：

YARNOLD, PR ^{[1
]}

SOLTYSIK, RC ^{[1
]}

机构：

[1] UNIV ILLINOIS,PSYCHOL,CHICAGO,IL 60680

来源：

DECISION SCIENCES | 1991年 / 22卷 / 04期

关键词：

DISCRETE PROGRAMMING; LINEAR STATISTICAL MODELS; MATHEMATICAL PROGRAMMING; AND STATISTICAL TECHNIQUES;

D O I：

10.1111/j.1540-5915.1991.tb00362.x

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

Optimal linear discriminant models maximize percentage accuracy for dichotomous classifications, but are rarely used because a theoretical framework that allows one to make valid statements about the statistical significance of the outcomes of such analyses does not exist. This paper describes an analytic solution for the theoretical distribution of optimal values for univariate optimal linear discriminant analysis, under the assumption that the data are random and continuous. We also present the theoretical distribution for sample sizes up to N= 30. The discovery of a statistical framework for evaluating the performance of optimal discriminant models should greatly increase their use by scientists in all disciplines. Copyright © 1991, Wiley Blackwell. All rights reserved

引用

页码：739 / 752

页数：14

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