পদ্ধতির তুলনা করুন

	Stochastic Frontier Model	দক্ষতা-ভিত্তিক ক্রমবিন্যাসের জন্য ডেটা এনভেলপমেন্ট অ্যানালাইসিস (CCR মডেল)
ক্ষেত্র	অর্থনীতি	সিদ্ধান্ত গ্রহণ
পরিবার	Regression model	MCDM
উদ্ভবের বছর	1977	1978
প্রবর্তক	Aigner, Lovell & Schmidt; Meeusen & van den Broeck	Charnes, A., Cooper, W. W., Rhodes, E.
ধরন	Parametric stochastic production/cost frontier with composed error	Non-parametric efficiency frontier (CCR model)
মৌলিক উৎস	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
অপর নাম	SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation	—
সম্পর্কিত	3	0
সারসংক্ষেপ	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.