Võrdle meetodeid

	Super-Efficiency Data Envelopment Analysis	Bootstrapi DEA: nihke korrigeerimine ja usaldusvahemikud efektiivsusskooridele	Võrgu andmete ümbrikmeetod (Network DEA)
Valdkond	Efektiivsusanalüüs	Efektiivsusanalüüs	Efektiivsusanalüüs
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	1993	1998	2000
Looja	Andersen & Petersen	Simar & Wilson	Färe & Grosskopf
Tüüp	Nonparametric linear programming model	Nonparametric efficiency estimation with bootstrap inference	Multi-stage nonparametric efficiency model
Algallikas	Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.	Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.	Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Rööpnimetused	Andersen-Petersen Model, Super-Radial DEA, Ranking DEA, Süper Etkinlik VZA	Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi	Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi
Seotud	2	2	2
Kokkuvõte	Super-Efficiency DEA is a nonparametric linear programming extension of classical Data Envelopment Analysis (DEA) introduced by Andersen and Petersen (1993). While standard DEA assigns a maximum efficiency score of 1.0 to all units on the efficient frontier, Super-Efficiency DEA allows efficient units to receive scores greater than 1.0 by temporarily removing the evaluated unit from the reference set. This modification enables full ranking of all decision-making units (DMUs), including those previously indistinguishable at the frontier.	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.	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.