Porovnat metody

	Bootstrap DEA: Korekce zkreslení a intervaly spolehlivosti pro skóre efektivity	Bootstrap Inference	Síťová analýza obalu dat (Network DEA)
Obor	Analýza efektivnosti	Statistika	Analýza efektivnosti
Rodina	Regression model	Regression model	Regression model
Rok vzniku	1998	1979	2000
Tvůrce	Simar & Wilson	Bradley Efron	Färe & Grosskopf
Typ	Nonparametric efficiency estimation with bootstrap inference	Resampling-based inference	Multi-stage nonparametric efficiency model
Původní zdroj	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.
Další názvy	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
Příbuzné	2	5	2
Shrnutí	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.