期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2013
卷号：12
期号：1
页码：17
出版社：Wayne State University
摘要：Principal component analysis reduces dimensionality; however, uncorrelated components imply the existence of variables with weights of opposite signs. This complicates the application in data envelopment analysis. To overcome problems due to signs, a modification to the component axes is proposed and was verified using Monte Carlo simulations.
关键词：Data envelopment analysis; principal component analysis; redundancy analysis; Monte Carlo simulation
