An objective-free outlying point detection model in data envelopment analysis

Chin Chia Kuo, Wen-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Data envelopment analysis (DEA) is a mathematical programming approach for benchmarking. Using extreme observations to identify superior performance makes DEA vulnerable to outliers. While there are many studies on detecting outliers for DEA, most focus on specific applications and objectives, e.g. orientation, and thus have limitations. We address the limits of conventional objective-dependent approaches and the need for an objective-free outlier detection mechanism, and propose an outlier detecting method that requires no pre-specified objectives. In addition to allowing for the identification of outliers, the method described is consistent with a relaxed set of DEA axioms.

Original languageEnglish
Pages (from-to)294-303
Number of pages10
JournalJournal of the Chinese Institute of Industrial Engineers
Issue number4
StatePublished - 1 Jul 2010


  • benchmarking
  • data envelopment analysis
  • outlier detection
  • sensitivity analysis

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