Módszerek összehasonlítása

	Bootstrap DEA	Bootstrap-becslés
Tudományterület	Hatékonyságelemzés	Statisztika
Módszercsalád	Regression model	Regression model
Keletkezés éve	1998	1979
Megalkotó	Simar & Wilson	Bradley Efron
Típus	Nonparametric efficiency estimation with bootstrap inference	Resampling-based inference
Alapmű	Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Alternatív nevek	Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Kapcsolódó	2	5
Összefoglaló	Bootstrap 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.