The performance of the linear and quadratic discriminant functions for three types of non-normal distribution
The performance of the linear and quadratic discriminant functions for three types of non-normal distribution
复制标题
三种非正态分布的线性和二次判别函数的性能
DOI：
10.1080/03610928508828970
复制
发表时间：
1985
影响因子：
0.8
通讯作者：
Yoshiharu Sato
中科院分区：
文献类型：
--
作者：
H. Nakanishi;Yoshiharu Sato
本文的目的是研究LDF（线性判别函数）和QDF（二次判别函数）在ML分类率基础上对三种类型的单变量和多变量非正态判别函数进行分类观测的性能。描述了单变量分布的理论和经验结果，并给出了多元分布的经验结果。它还表明，每个总体的偏度符号和峰度对两个判别函数的性能具有重要影响。总体特征分类率的变化很大程度上取决于样本量。对于大尺度总体分布，如果样本量足够，QDF 的性能优于 LDF。我们展示了两个判别函数之间的选择关系作为应用。
The purpose of thls paper is to investlgate the performance of the LDF (linear discrlmlnant functlon) and QDF (quadratic dlscrminant functlon) for classlfylng observations from the three types of univariate and multivariate non-normal dlstrlbutlons on the basls of the mlsclasslficatlon rate. The theoretical and the empirical results are described for unlvariate distributions, and the empirical results are presented for multivariate distributions. It 1s also shown that the sign of the skewness of each population and the kurtosis have essential effects on the performance of the two discriminant functions. The variations of the populatlon speclflc mlsclasslflcatlon rates are greatly depend on the sample slze. For the large dlmenslonal populatlon dlstributlons, if the sample sizes are sufflclent, the QDF performs better than the LDF. We show the crlterla of a cholce between the two discriminant functions as an application.