Journal
JOURNAL OF MODERN APPLIED STATISTICAL METHODS
Volume 12, Issue 1, Pages 128-147Publisher
WAYNE STATE UNIV PRESS
DOI: 10.22237/jmasm/1367381760
Keywords
Data envelopment analysis; principal component analysis; redundancy analysis; Monte Carlo simulation
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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.
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