Võrdle meetodeid

	Bootstrapi DEA: nihke korrigeerimine ja usaldusvahemikud efektiivsusskooridele	Bootstrap-meetodist	Võrgu andmete ümbrikmeetod (Network DEA)
Valdkond	Efektiivsusanalüüs	Statistika	Efektiivsusanalüüs
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	1998	1979	2000
Looja	Simar & Wilson	Bradley Efron	Färe & Grosskopf
Tüüp	Nonparametric efficiency estimation with bootstrap inference	Resampling-based inference	Multi-stage nonparametric efficiency model
Algallikas	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.	Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Rööpnimetused	Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi
Seotud	2	5	2
Kokkuvõte	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.	Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model.