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Solidna regresja kwantyl-na-kwantylu (RQQR)×Regresja kwantylowa na kwantylach (QQ)×
DziedzinaEkonometriaEkonometria
RodzinaRegression modelRegression model
Rok powstania2015–2020s2015
TwórcaSim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literatureSim and Zhou
TypNonparametric quantile regressionNonparametric quantile regression
Źródło pierwotneSim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8. DOI ↗Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8. DOI ↗
Inne nazwyRQQR, robust QQ regression, robust quantile-on-quantile, outlier-robust QQRQQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression
Pokrewne36
PodsumowanieRobust 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.
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ScholarGatePorównaj metody: Robust Quantile-on-Quantile Regression · Quantile-on-Quantile Regression. Pobrano 2026-06-17 z https://scholargate.app/pl/compare