Efficiency evaluation of data warehouse operations
Decision Support Systems
Measuring biodiversity performance: A conditional efficiency measurement approach
Environmental Modelling & Software
Technical efficiency in Saudi banks
Expert Systems with Applications: An International Journal
Exploring the efficiency and effectiveness in global e-retailing companies
Computers and Operations Research
A Multiple Stage Approach for Performance Improvement of Primary Healthcare Practice
Journal of Medical Systems
Journal of Medical Systems
Bootstrapping profit change: An application to Spanish banks
Computers and Operations Research
Industry performance evaluation with the use of financial ratios: An application of bootstrapped DEA
Expert Systems with Applications: An International Journal
ICT capital and labour productivity growth: A non-parametric analysis of 14 OECD countries
Telecommunications Policy
Expert Systems with Applications: An International Journal
Robust Optimization in Simulation: Taguchi and Krige Combined
INFORMS Journal on Computing
Measuring Eco-Inefficiency: A New Frontier Approach
Operations Research
Common weights data envelopment analysis with uncertain data: A robust optimization approach
Computers and Industrial Engineering
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Efficiency scores of production units are generally measured relative to an estimated pro-duction frontier. Nonparametric estimators (DEA, FDH,脗·脗·脗·) 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.