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.
Yap, Grace Lee Ching; Ismail, Wan Rosmanira; and Isa, Zaidi
"An Alternative Approach to Reduce Dimensionality in Data Envelopment Analysis,"
Journal of Modern Applied Statistical Methods:
1, Article 17.
Available at: http://digitalcommons.wayne.edu/jmasm/vol12/iss1/17