标题: | A unified model for detecting efficient and inefficient outliers in data envelopment analysis |
作者: | Chen, Wen-Chih Johnson, Andrew L. 工业工程与管理学系 Department of Industrial Engineering and Management |
关键字: | Data envelopment analysis;Outlier;Post analysis |
公开日期: | 1-二月-2010 |
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. (C) 2009 Elsevier Ltd. All rights reserved. |
URI: | http://dx.doi.org/10.1016/j.cor.2009.06.010 http://hdl.handle.net/11536/5891 |
ISSN: | 0305-0548 |
DOI: | 10.1016/j.cor.2009.06.010 |
期刊: | COMPUTERS & OPERATIONS RESEARCH |
Volume: | 37 |
Issue: | 2 |
起始页: | 417 |
结束页: | 425 |
显示于类别: | Articles |
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