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| Regresja kwantylowa bayesowska× | Regresja kwantylowa odporna× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2001–2011 | 1993–1997 |
| Twórca≠ | Kozumi & Kobayashi; building on Yu & Moyeed (2001) | Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997) |
| Typ≠ | Bayesian semiparametric regression | Robust semiparametric regression |
| Źródło pierwotne≠ | Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. DOI ↗ | Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275 |
| Inne nazwy | BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression | robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Bayesian Quantile Regression estimates the full posterior distribution of regression coefficients at any chosen quantile of the outcome. By combining the asymmetric Laplace likelihood with prior distributions over the coefficients, it delivers uncertainty-quantified estimates of conditional quantiles — such as the median, the 10th, or the 90th percentile — without assuming Gaussian errors. | 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. |
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