Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models
提出在非参数前沿模型（如DEA、FDH）中使用自助法分析效率得分对抽样变异的敏感性，并给出通用方法论，以电力厂投入效率为例说明。
Efficiency scores of production units are generally measured relative to an estimated production frontier. Nonparametric estimators (DEA, FDH, ⋯) are based on a finite sample of observed production units. The bootstrap is one easy way to analyze the sensitivity of efficiency scores relative to the sampling variations of the estimated frontier. The main point in order to validate the bootstrap is to define a reasonable data-generating process in this complex framework and to propose a reasonable estimator of it. This paper provides a general methodology of bootstrapping in nonparametric frontier models. Some adapted methods are illustrated in analyzing the bootstrap sampling variations of input efficiency measures of electricity plants.