A unified model for detecting efficient and inefficient outliers in data envelopment analysis

Wen-Chih Chen*, Andrew L. Johnson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Data envelopment analysis (DEA) uses extreme observations to identify superior performance, making it vulnerable to outliers. This paper develops a unified model to identify both efficient and inefficient outliers in DEA. Finding both types is important since many post analyses, after measuring efficiency, depend on the entire distribution of efficiency estimates. Thus, outliers that are distinguished by poor performance can significantly alter the results. Besides allowing the identification of outliers, the method described is consistent with a relaxed set of DEA axioms. Several examples demonstrate the need for identifying both efficient and inefficient outliers and the effectiveness of the proposed method. Applications of the model reveal that observations with low efficiency estimates are not necessarily outliers. In addition, a strategy to accelerate the computation is proposed that can apply to influential observation detection.

Original languageEnglish
Pages (from-to)417-425
Number of pages9
JournalComputers and Operations Research
Volume37
Issue number2
DOIs
StatePublished - 1 Feb 2010

Keywords

  • Data envelopment analysis
  • Outlier
  • Post analysis

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