เปรียบเทียบวิธี

	การถดถอยแบบทนทาน	Lasso Regression	การถดถอยกำลังสองตัดแต่งน้อยที่สุด (Least Trimmed Squares: LTS)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)
สาขาวิชา	สถิติศาสตร์	การเรียนรู้ของเครื่อง	สถิติศาสตร์	เศรษฐมิติ
ตระกูล	Regression model	Machine learning	Regression model	Regression model
ปีกำเนิด	1964	1996	1984	2019
ผู้ริเริ่ม	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Tibshirani, R.	Peter J. Rousseeuw	Wooldridge (textbook treatment); classical least squares
ประเภท	Regression with outlier resistance	Regularized linear regression (L1 penalty)	Robust linear regression	Linear regression
แหล่งต้นตำรับ	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
ชื่อเรียกอื่น	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	LTS, least trimmed squares regression, trimmed least squares, robust regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ที่เกี่ยวข้อง	6	4	5	5
สรุป	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.	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).