เปรียบเทียบวิธี

	การประมาณค่าส่วนเบี่ยงเบนสัมบูรณ์มัธยฐาน (MAD)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	การถดถอยควอนไทล์	Ridge Regression
สาขาวิชา	สถิติศาสตร์	เศรษฐมิติ	เศรษฐมิติ	การเรียนรู้ของเครื่อง
ตระกูล	Regression model	Regression model	Regression model	Machine learning
ปีกำเนิด	1974	2019	1978	1970
ผู้ริเริ่ม	Hampel (influence-curve treatment); classical robust statistics	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett	Hoerl, A.E. & Kennard, R.W.
ประเภท	Robust scale estimator	Linear regression	Conditional quantile regression	L2-regularized linear regression
แหล่งต้นตำรับ	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	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.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
ชื่อเรียกอื่น	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
ที่เกี่ยวข้อง	5	5	5	4
สรุป	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.