Comparer des méthodes

	Régression Quantile Bayésienne	Régression quantile
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	2001–2011	1978
Auteur d'origine	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	Koenker & Bassett
Type	Bayesian semiparametric regression	Conditional quantile regression
Source fondatrice	Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	6	5
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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.