Data Envelopment Analysis (DEA) is a deterministic method for the aggregation of multidimensional measures and subsequent efficiency analysis. Due to its inherent determinism, however, it reacts sensitively to outliers in datasets. Existing methods for identifying such outliers have two main disadvantages. First, from a more conceptional point of view, a uniform definition of an outlier is missing. Second, there are technical disadvantages of each method. For instance, arbitrarily limited values have to be set by the user, like the amount of efficiency value from which on a decision making unit is regarded as an outlier. This paper initially presents a definition of outliers, which explicitly takes the specifics of DEA into account. Based on this definition, an approach for identifying outliers in DEA is introduced which explicitly tackles the technical disadvantages and takes them into account in the developed algorithm. The plausibility of this approach is validated on the basis of empirical examples from performance measurement at the university level.

中文翻译：

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数据包络分析中异常值的识别

数据包络分析（DEA）是一种确定性方法，用于汇总多维度量并进行后续效率分析。但是，由于其固有的确定性，它对数据集中的异常值敏感。识别此类异常值的现有方法有两个主要缺点。首先，从更具概念的角度来看，缺少对异常值的统一定义。其次，每种方法都有技术缺点。例如，用户必须设置任意限制的值，就像效率值的大小一样，决策单位将其视为离群值。本文最初提出了异常值的定义，该定义明确考虑了DEA的细节。根据这个定义，引入了一种在DEA中识别异常值的方法，该方法可以明确解决技术上的缺点，并在开发的算法中将它们考虑在内。该方法的合理性已根据大学水平绩效评估中的经验示例进行了验证。