Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Stochastic Frontier Model× | Analýza obálkování dat (model CCR) pro hodnocení založené na efektivitě× | |
|---|---|---|
| Obor≠ | Ekonomie | Rozhodování |
| Rodina≠ | Regression model | MCDM |
| Rok vzniku≠ | 1977 | 1978 |
| Tvůrce≠ | Aigner, Lovell & Schmidt; Meeusen & van den Broeck | Charnes, A., Cooper, W. W., Rhodes, E. |
| Typ≠ | Parametric stochastic production/cost frontier with composed error | Non-parametric efficiency frontier (CCR model) |
| Původní zdroj≠ | 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. DOI ↗ | Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research DOI ↗ |
| Další názvy≠ | SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation | — |
| Příbuzné≠ | 3 | 0 |
| Shrnutí≠ | 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. |
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