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	Robust Kvantilregression	Bayesiansk kvantilregression
Ämnesområde	Statistik	Statistik
Familj	Regression model	Regression model
Ursprungsår	1993–1997	2001–2011
Upphovsperson	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)	Kozumi & Kobayashi; building on Yu & Moyeed (2001)
Typ	Robust semiparametric regression	Bayesian semiparametric regression
Ursprungskälla	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275	Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.
Alias	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression
Närliggande	6	6
Sammanfattning	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.	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.