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Sensitivity and stability analysis on the first and second levels of efficiency score relative to data error

机译:关于效率得分的第一和第二级相对于数据误差的敏感性和稳定性分析

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In data envelopment analysis (DEA), efficient decision making units (DMUs) are of primary importance as they define the efficient frontier. The current paper develops a new sensitivity analysis approach for a category DMUs and finds the stability radius for all efficient DMUs. By means of combining some classic DEA models and with the condition that the efficiency scores of efficient DMUs remain unchanged, we are able to determine what perturbations of the data can be tolerated before efficient DMUs become inefficient. Our approach generalizes the conventional sensitivity analysis approach in which the inputs of efficient DMUs increase and their outputs decrease, while the inputs of inefficient DMUs decrease and their outputs increase. We find the maximum quantity of perturbations of data so that all first level efficient DMUs remain at the same level.
机译:在数据包络分析(DEA)中,有效决策单元(DMU)至关重要,因为它们定义了有效边界。本文为类别DMU开发了一种新的灵敏度分析方法,并找到了所有有效DMU的稳定半径。通过结合一些经典的DEA模型并在有效DMU的效率得分保持不变的条件下,我们能够确定在有效DMU变得无效之前可以容忍哪些数据扰动。我们的方法概括了传统的灵敏度分析方法,其中有效DMU的输入增加而其输出减少,而无效DMU的输入减少而其输出增加。我们找到了最大的数据扰动量,以便所有第一级有效的DMU都保持在同一级别。

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