Vertaile menetelmiä

	M-estimaattorit (Robustin regressio)	OLS-regressio (Ordinary Least Squares)
Tieteenala	Tilastotiede	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2009	2019
Kehittäjä	Peter J. Huber	Wooldridge (textbook treatment); classical least squares
Tyyppi	Robust linear regression	Linear regression
Alkuperäislähde	Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	m-estimation, huber regression, robust m-regression, M-Tahmin Ediciler	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	5	5
Tiivistelmä	M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.	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).