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| Robuste Quantil-auf-Quantil-Regression (RQQR)× | Robuste Regression× | |
|---|---|---|
| Fachgebiet≠ | Ökonometrie | Statistik |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2015–2020s | 1964 |
| Urheber≠ | Sim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literature | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| Typ≠ | Nonparametric quantile regression | Regression with outlier resistance |
| Wegweisende Quelle≠ | Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| Aliasnamen | RQQR, robust QQ regression, robust quantile-on-quantile, outlier-robust QQR | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| Verwandt≠ | 3 | 6 |
| Zusammenfassung≠ | 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. | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. |
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