مقایسهٔ روش‌ها

	تخمین‌گر W: رگرسیون مقاوم (وزن‌های ولش / توکی بای‌اسکوئر)	برآوردگر MM برای رگرسیون استوار	رگرسیون حداقل مربعات معمولی (OLS)
حوزه	آمار	آمار	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1974	1987	2019
پدیدآور	Beaton & Tukey (bisquare weight); Welsch (Welsch weight)	Victor J. Yohai	Wooldridge (textbook treatment); classical least squares
نوع	Robust regression (redescending M-estimator)	Robust linear regression	Linear regression
منبع بنیادین	Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
نام‌های دیگر	Tukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare)	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
مرتبط	4	5	5
خلاصه	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.	The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.	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).