Comparer des méthodes

	Régression par Moindres Carrés Ordinaires (MCO)	Estimateur Tau (τ) de régression
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	2019	1988
Auteur d'origine	Wooldridge (textbook treatment); classical least squares	Yohai & Zamar
Type	Linear regression	Robust linear regression
Source fondatrice	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Yohai, V. J., & Zamar, R. H. (1988). High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale. Journal of the American Statistical Association, 83(402), 406-413.
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	tau regression estimator, robust tau regression, Tau-Tahmin Edici
Apparentées	5	4
Résumé	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 Tau estimator is a robust linear regression method introduced by Yohai and Zamar in 1988 that fits the model by minimising an efficient τ-scale of the residuals. It builds on the scale estimate of the S-estimator to combine a high breakdown point with high statistical efficiency, and is often used as an alternative to the MM-estimator in small samples.