Comparer des méthodes

	Régression Quantile Bayésienne	Régression Robuste Bayésienne
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2001–2011	1993
Auteur d'origine	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	Geweke (1993); Gelman et al. (2013)
Type	Bayesian semiparametric regression	Bayesian regression with heavy-tailed errors
Source fondatrice	Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.
Alias	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression	Bayesian heavy-tailed regression, Bayesian Student-t regression, robust Bayesian linear model, BRR
Apparentées	6	6
Résumé	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.	Bayesian Robust Regression replaces the Gaussian error assumption of ordinary linear regression with a heavy-tailed distribution — most commonly the Student-t — and estimates all parameters in a Bayesian framework. The heavier tails give outliers less influence on the fitted line, yielding stable coefficient estimates and honest uncertainty intervals even when the data contain unusual observations.