Módszerek összehasonlítása

	Data Envelopment Analysis (Productivity)	Adatburkoló Elemzés (CCR modell) hatékonyság-alapú rangsoroláshoz	Sztochasztikus határanalízis (SFA)
Tudományterület	Közgazdaságtan	Döntéshozatal	Ökonometria
Módszercsalád	Process / pipeline	MCDM	Regression model
Keletkezés éve	1978	1978	1977
Megalkotó	Charnes, Cooper & Rhodes (building on Farrell 1957)	Charnes, A., Cooper, W. W., Rhodes, E.	Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels
Típus	Nonparametric linear-programming efficiency frontier	Non-parametric efficiency frontier (CCR model)	Frontier regression model
Alapmű	Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.	Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research	Aigner, D., Lovell, C.A.K. & Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6(1), 21–37.
Alternatív nevek	DEA Efficiency Analysis, Nonparametric Frontier Efficiency, CCR/BCC Efficiency Measurement, Production Frontier DEA	—	SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA)
Kapcsolódó	5	0	3
Összefoglaló	Data envelopment analysis (DEA) is a nonparametric, linear-programming technique for measuring the relative productive efficiency of comparable units — firms, plants, hospitals, schools, bank branches — that convert multiple inputs into multiple outputs. Introduced by Charnes, Cooper, and Rhodes in 1978 and rooted in Farrell's 1957 work on efficiency measurement, it constructs a best-practice frontier that envelops the observed data and scores each unit by its distance to that frontier, requiring no assumed functional form for the production technology.	DEA (Data Envelopment Analysis (CCR model) for efficiency-based ranking) is a dea multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W., Rhodes, E. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	Stochastic Frontier Analysis is a frontier regression model, introduced by Aigner, Lovell and Schmidt in 1977, that estimates a production, cost, or profit function while separating each unit's technical inefficiency from ordinary statistical noise. It splits the error term into a symmetric random component and a one-sided inefficiency component, producing firm- or country-level efficiency scores.