数据包络分析

数据包络分析（Data envelopment analysis，DEA）是运筹学和经济学中一种非参数化的评估生产前沿的方法。  这一分析方法广泛应用于多个领域，包括国际银行业、经济可持续性研究、警务运营，以及物流管理。 DEA还被用于评估自然语言处理模型的性能，在机器学习领域也展现出诸多应用前景。

**简述**
数据包络分析（DEA）是一种实证测量决策单元生产效率（DMU）的方法。尽管起源于经济学的生产理论，但如今已广泛应用于运营管理的标杆评估。 在标杆分析中，研究者选择一系列指标，对制造业和服务业的运营绩效进行比较。243–285
与必须预先设定生产或成本函数的参数化方法不同，非参数化方法仅根据可获得的数据比较可行的投入产出组合。 作为最常用的非参数化方法之一，数据包络分析得名于其对数据集中高效决策单元的“包络”特性。在这一方法中，经验上最高效的决策单元构成了“最佳实践前沿”，其他所有决策单元都与之对标。
数据包络分析之所以广受欢迎，主要有三个原因：
# 假设条件相对较少
# 能够对多维度的投入和产出进行标杆比较
# 计算简便，可以通过线性规划方法直接求解效率比率
不同于传统方法，DEA并不追求构建严格的“生产前沿”，而是试图找出实践中最为高效的运营模式。

**历史**
DEA最早可追溯到1978年。在Farrell的研究基础上， 查恩斯、库珀和罗德斯运用线性规划，首次实证性地估算了生产技术前沿。在德国，类似方法此前已被用于估算研发及其他生产要素的边际生产率。此后，DEA迅速成为学术界研究的热点，大量专着和期刊论文涌现。
从最初的CCR模型（以创始人查恩斯、库珀、罗德斯命名）开始， 学者们不断拓展DEA的应用。这些扩展包括多个方面：调整模型隐含假设、区分技术效率与配置效率、引入输入输出的有限替代性、考虑规模收益变化，以及开发更复杂的分析技术，如随机DEA和交叉效率分析等。

**脚注**

**参考文献**
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**外部链接**
 Data Envelopment Analysis官方网站- Journal of Productivity Analysis官方网站