เปรียบเทียบวิธี

	การถดถอยแบบแข็งแกร่งด้วย W-Estimator (Welsch / Tukey Bisquare)	การถดถอยกำลังสองน้อยที่สุดสามัญ (OLS)	ตัวประมาณค่าแบบ Theil-Sen
สาขาวิชา	สถิติศาสตร์	เศรษฐมิติ	สถิติศาสตร์
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	1974	2019	1968
ผู้ริเริ่ม	Beaton & Tukey (bisquare weight); Welsch (Welsch weight)	Wooldridge (textbook treatment); classical least squares	Henri Theil (1950); P. K. Sen (1968)
ประเภท	Robust regression (redescending M-estimator)	Linear regression	Robust linear regression
แหล่งต้นตำรับ	Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389.
ชื่อเรียกอื่น	Tukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator
ที่เกี่ยวข้อง	4	5	6
สรุป	The W-estimator is a family of robust M-estimator variants for linear regression that use the Tukey bisquare and Welsch weight functions, introduced in the line of work going back to Beaton and Tukey (1974). Because its weights fall rapidly toward zero as a residual grows, it resists outliers more strongly than the Huber M-estimator.	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).	The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%.