Regression modelEfficiency analysis
Bootstrap DEA：效率得分的偏差校正与置信区间
Bootstrap Data Envelopment Analysis (Bootstrap DEA) 是一种基于重采样的标准DEA的扩展，可为效率得分提供统计上有效的推断。该方法由Simar和Wilson于1998年提出，通过构建自助法置信区间和偏差校正的效率估计值来解决经典DEA的核心弱点——即无法量化估计得分中的不确定性——其方法是通过反复重采样伪前沿来实现的。
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来源
- Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61. DOI: 10.1287/mnsc.44.1.49 ↗
如何引用本页
ScholarGate. (2026, June 2). Bootstrap Data Envelopment Analysis. ScholarGate. https://scholargate.app/zh/efficiency-analysis/bootstrap-dea
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