Abstract
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.
DOI
10.22237/jmasm/1367381760
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
