An alternative approach to reduce dimensionality in data envelopment analysis

Abstract

Principal component analysis reduces dimensionality; however, uncorrelated components imply the existence of variables with weights of opposite signs. This complicates the applicationin data envelopment analysis. To overcome problems due to signs, a modification to the component axes is proposed and was verified using Monte Carlo simulations. © 2013 JMASM, Inc.