विधियों की तुलना करें

	मजबूत क्वांटाइल प्रतिगमन	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	सांख्यिकी	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1993–1997	2019
प्रवर्तक	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Wooldridge (textbook treatment); classical least squares
प्रकार	Robust semiparametric regression	Linear regression
मौलिक स्रोत	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
उपनाम	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	6	5
सारांश	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).