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Bootstrap DEA: Bias Correction and Confidence Intervals for Efficiency Scores×Bootstrap-inferens×
FagfeltEffektivitetsanalyseStatistikk
FamilieRegression modelRegression model
Opprinnelsesår19981979
OpphavspersonSimar & WilsonBradley Efron
TypeNonparametric efficiency estimation with bootstrap inferenceResampling-based inference
Opprinnelig kildeSimar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61. DOI ↗Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗
AliasBootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizibootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Relaterte25
SammendragBootstrap Data Envelopment Analysis (Bootstrap DEA) is a resampling-based extension of standard DEA that provides statistically valid inference for efficiency scores. Introduced by Simar and Wilson in 1998, it addresses the core weakness of classical DEA — its inability to quantify uncertainty in estimated scores — by constructing bootstrap confidence intervals and bias-corrected efficiency estimates from repeatedly resampled pseudo-frontiers.Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.
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ScholarGateSammenlign metoder: Bootstrap DEA · Bootstrap Inference. Hentet 2026-06-15 fra https://scholargate.app/no/compare