Evaluation of Five Discrimination Procedures for Binary Variables
Evaluation of Five Discrimination Procedures for Binary Variables
复制标题
二元变量五种判别程序的评估
DOI：
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发表时间：
1973
期刊：
影响因子：
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通讯作者：
D. Moore
中科院分区：
文献类型：
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作者：
D. Moore
本文讨论和评价了五种二元变量小样本判别方法。有两个程序是特定于二元变量的，它们基于对多项式概率的一阶和二阶近似。基于全多项式模型的第三种方法是完全通用的。第四和第五步是线性和二次判别式。评估是根据蒙特卡罗抽样确定的观测对数似然比和真实对数似然比之间的平均实际误差和平均相关性进行的。引入对数似然比“反转”的概念来解释这一结果。
Abstract Five procedures for discrimination with binary variables and small samples are discussed and evaluated. Two procedures are specific for binary variables and are based on first and second order approximations to multinomial probabilities. The third procedure, based on the full multinomial model, is completely general. The fourth and fifth procedures are the linear and quadratic discriminants. Evaluation is in terms of mean actual error and mean correlation between observed and true log likelihood ratios determined by Monte Carlo sampling. The concept of a “reversal” in log likelihood ratios is introduced to explain the results.