Data envelopment analysis (DEA) theory formulates a number of desirable properties that DEA models should satisfy. Among these, indication, strict monotonicity, and strong efficiency of projections tend to be grouped together in the sense that, in individual models, typically, either all three are satisfied or all three fail at the same time. Specifically, in slacks-based graph models, the three properties are always met; in path-based models, such as radial models, directional distance function models, and the hyperbolic function model, the three properties, with some minor exceptions, typically all fail.

中文翻译：

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关于一类基于路径的数据包络分析模型中投影的指示、严格单调性和效率


数据包络分析 (DEA) 理论阐述了 DEA 模型应满足的许多理想属性。其中，指示性、严格单调性和预测的高效率往往被归为一组，即在单个模型中，通常要么所有三个都满足，要么所有三个同时失败。具体来说，在基于 slack 的图模型中，始终满足这三个属性；在基于路径的模型中，例如径向模型、方向距离函数模型和双曲函数模型，除了一些小的例外之外，这三个属性通常都会失败。