數據包絡分析

數據包絡分析（Data envelopment analysis，DEA）是運籌學和經濟學中一種非參數化的評估生產前沿的方法。  這一分析方法廣泛應用於多個領域，包括國際銀行業、經濟可持續性研究、警務運營，以及物流管理。 DEA還被用於評估自然語言處理模型的性能，在機器學習領域也展現出諸多應用前景。

**簡述**
數據包絡分析（DEA）是一種實證測量決策單元生產效率（DMU）的方法。儘管起源於經濟學的生產理論，但如今已廣泛應用於運營管理的標杆評估。 在標杆分析中，研究者選擇一系列指標，對製造業和服務業的運營績效進行比較。243–285
與必須預先設定生產或成本函數的參數化方法不同，非參數化方法僅根據可獲得的數據比較可行的投入產出組合。 作為最常用的非參數化方法之一，數據包絡分析得名於其對數據集中高效決策單元的“包絡”特性。在這一方法中，經驗上最高效的決策單元構成了“最佳實踐前沿”，其他所有決策單元都與之對標。
數據包絡分析之所以廣受歡迎，主要有三個原因：
# 假設條件相對較少
# 能夠對多維度的投入和產出進行標杆比較
# 計算簡便，可以通過線性規劃方法直接求解效率比率
不同於傳統方法，DEA並不追求構建嚴格的“生產前沿”，而是試圖找出實踐中最為高效的運營模式。

**歷史**
DEA最早可追溯到1978年。在Farrell的研究基礎上， 查恩斯、庫珀和羅德斯運用線性規劃，首次實證性地估算了生產技術前沿。在德國，類似方法此前已被用於估算研發及其他生產要素的邊際生產率。此後，DEA迅速成為學術界研究的熱點，大量專著和期刊論文湧現。
從最初的CCR模型（以創始人查恩斯、庫珀、羅德斯命名）開始， 學者們不斷拓展DEA的應用。這些擴展包括多個方面：調整模型隱含假設、區分技術效率與配置效率、引入輸入輸出的有限替代性、考慮規模收益變化，以及開發更復雜的分析技術，如隨機DEA和交叉效率分析等。

**腳註**

**參考文獻**
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**外部鏈接**
 Data Envelopment Analysis官方網站- Journal of Productivity Analysis官方網站