Compară metode

	Regresia robustă a cuantilelor	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Statistică	Econometrie
Familie	Regression model	Regression model
Anul apariției	1993–1997	2019
Autorul original	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Wooldridge (textbook treatment); classical least squares
Tip	Robust semiparametric regression	Linear regression
Sursa seminală	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Denumiri alternative	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Înrudite	6	5
Rezumat	Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions.	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).