Võrdle meetodeid

	Tavaline vähimruutude (OLS) regressioon	Tau (τ) estimaator regressioonis
Valdkond	Ökonomeetria	Statistika
Perekond	Regression model	Regression model
Tekkeaasta	2019	1988
Looja	Wooldridge (textbook treatment); classical least squares	Yohai & Zamar
Tüüp	Linear regression	Robust linear regression
Algallikas	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.
Rööpnimetused	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	tau regression estimator, robust tau regression, Tau-Tahmin Edici
Seotud	5	4
Kokkuvõte	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.