เปรียบเทียบวิธี

	Data Envelopment Analysis (Productivity)	Stochastic Frontier Model
สาขาวิชา	เศรษฐศาสตร์	เศรษฐศาสตร์
ตระกูล	Process / pipeline	Regression model
ปีกำเนิด	1978	1977
ผู้ริเริ่ม	Charnes, Cooper & Rhodes (building on Farrell 1957)	Aigner, Lovell & Schmidt; Meeusen & van den Broeck
ประเภท	Nonparametric linear-programming efficiency frontier	Parametric stochastic production/cost frontier with composed error
แหล่งต้นตำรับ	Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.	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.
ชื่อเรียกอื่น	DEA Efficiency Analysis, Nonparametric Frontier Efficiency, CCR/BCC Efficiency Measurement, Production Frontier DEA	SFM, Stochastic Production Frontier, Composed-Error Frontier Model, Parametric Frontier Estimation
ที่เกี่ยวข้อง	5	3
สรุป	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.	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.