A procedure for ranking efficient units in data envelopment analysis
Management Science
An analysis of the performance of university-affiliated credit unions
Computers and Operations Research - Special issue on data envelopment analysis
Stochastic frontier analysis
A Statistical Test For Nested Radial Dea Models
Operations Research
Evaluating alternative DEA models used to control for non-discretionary inputs
Computers and Operations Research
Evaluation of information technology investment: a data envelopment analysis approach
Computers and Operations Research
A computational study of DEA with massive data sets
Computers and Operations Research
Efficiency persistence of bank and thrift CEOs using data envelopment analysis
Computers and Operations Research
A procedure for large-scale DEA computations
Computers and Operations Research
Large-scale Internet benchmarking: Technology and application in warehousing operations
Computers in Industry
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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.