Including principal component weights to improve discrimination in data envelopment analysis

N. Adler, B. Golany*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

135 Scopus citations

Abstract

This research further develops the combined use of principal component analysis (PCA) and data envelopment analysis (DEA). The aim is to reduce the curse of dimensionality that occurs in DEA when there is an excessive number of inputs and outputs in relation to the number of decision-making units. Three separate PCA-DEA formulations are developed in the paper utilising the results of PCA to develop objective, assurance region type constraints on the DEA weights. The first model applies PCA to grouped data representing similar themes, such as quality or environmental measures. The second model, if needed, applies PCA to all inputs and separately to all outputs, thus further strengthening the discrimination power of DEA. The third formulation searches for a single set of global weights with which to fully rank all observations. In summary, it is clear that the use of principal components can noticeably improve the strength of DEA models. Journal of the Operational Research Society.

Original languageAmerican English
Pages (from-to)985-991
Number of pages7
JournalJournal of the Operational Research Society
Volume53
Issue number9
DOIs
StatePublished - Sep 2002

Bibliographical note

Funding Information:
Acknowledgements—The authors would like to thank two anonymous referees for very helpful comments on an earlier version of this paper. N.A. would also like to thank the Recanati Foundation for partial support of this research.

Keywords

  • Assurance regions
  • Data envelopment analysis
  • Performance measurement
  • Principal component analysis
  • Ranking

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