Сравнение на методи

	Регресия „квантил върху квантил“ (Robust Quantile-on-Quantile Regression, RQQR)	Квантил-върху-квантилна (КвКв) регресия
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2015–2020s	2015
Създател	Sim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literature	Sim and Zhou
Тип	Nonparametric quantile regression	Nonparametric quantile regression
Основополагащ източник	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8.	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.
Други названия	RQQR, robust QQ regression, robust quantile-on-quantile, outlier-robust QQR	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression
Свързани	3	6
Резюме	Robust Quantile-on-Quantile Regression extends the QQ framework of Sim and Zhou (2015) by adding resistance to outliers and heavy-tailed distributions. It estimates how each quantile of one variable responds to each quantile of another, producing a full dependence surface while guarding against leverage points that can distort standard QQ estimates.	Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression.