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

Wen-Chih Chen*, Andrew L. Johnson

*Corresponding author for this work

研究成果: Article同行評審

23 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)417-425
頁數9
期刊Computers and Operations Research
37
發行號2
DOIs
出版狀態Published - 1 二月 2010

指紋 深入研究「A unified model for detecting efficient and inefficient outliers in data envelopment analysis」主題。共同形成了獨特的指紋。

引用此