Сравнение на методи

	Robust Quantile Regression	Квантилна регресия
Област	Статистика	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1993–1997	1978
Създател	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Koenker & Bassett
Тип	Robust semiparametric regression	Conditional quantile regression
Основополагащ източник	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Други названия	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	conditional quantile regression, regression quantiles, Kantil Regresyon
Свързани	6	5
Резюме	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.