Compară metode

	Regresia Cuantilă Bayesiană	Regresia robustă a cuantilelor
Domeniu	Statistică	Statistică
Familie	Regression model	Regression model
Anul apariției	2001–2011	1993–1997
Autorul original	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)
Tip	Bayesian semiparametric regression	Robust semiparametric regression
Sursa seminală	Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.	Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275
Denumiri alternative	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression	robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR
Înrudite	6	6
Rezumat	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.