เปรียบเทียบวิธี

	การวิเคราะห์ชายแดนสโตแคสติก (SFA)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	การถดถอยควอนไทล์
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	1977	2019	1978
ผู้ริเริ่ม	Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
ประเภท	Frontier regression model	Linear regression	Conditional quantile regression
แหล่งต้นตำรับ	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
ชื่อเรียกอื่น	SFA, stochastic frontier model, stochastic production frontier, Stokastik Sınır Analizi (SFA)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
ที่เกี่ยวข้อง	3	5	5
สรุป	Stochastic Frontier Analysis is a frontier regression model, introduced by Aigner, Lovell and Schmidt in 1977, that estimates a production, cost, or profit function while separating each unit's technical inefficiency from ordinary statistical noise. It splits the error term into a symmetric random component and a one-sided inefficiency component, producing firm- or country-level efficiency scores.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.