Sammenlign metoder

	Stochastic Frontier Model	Data Envelopment Analysis (CCR-modelen) til effektivitetsbaseret rangering
Fagområde	Økonomi	Beslutningstagning
Familie	Regression model	MCDM
Oprindelsesår	1977	1978
Ophavsperson	Aigner, Lovell & Schmidt; Meeusen & van den Broeck	Charnes, A., Cooper, W. W., Rhodes, E.
Type	Parametric stochastic production/cost frontier with composed error	Non-parametric efficiency frontier (CCR model)
Oprindelig kilde	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.	Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research
Aliasser	SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation	—
Relaterede	3	0
Resumé	The stochastic frontier model is a parametric method for estimating productive efficiency that separates a producer's shortfall from best practice into two parts: genuine inefficiency and random noise. Introduced independently in 1977 by Aigner, Lovell, and Schmidt and by Meeusen and van den Broeck, it specifies a production (or cost) function with a composed error term — a symmetric disturbance for luck and measurement error plus a one-sided, non-negative term for inefficiency — and estimates it by maximum likelihood, yielding firm-specific efficiency scores that, unlike deterministic methods, are robust to statistical noise.	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.