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Stochastic Frontier Model×DEA×
CampoEconomíaToma de decisiones
FamiliaRegression modelMCDM
Año de origen19771978
Autor originalAigner, Lovell & Schmidt; Meeusen & van den BroeckCharnes, A., Cooper, W. W., Rhodes, E.
TipoParametric stochastic production/cost frontier with composed errorNon-parametric efficiency frontier (CCR model)
Fuente seminalAigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. DOI ↗Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research DOI ↗
AliasSFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation
Relacionados30
ResumenThe 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.
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ScholarGateComparar métodos: Stochastic Frontier Model · DEA. Recuperado el 2026-06-24 de https://scholargate.app/es/compare