방법 비교

	베이지안 분위수 회귀	조건부 분위수 회귀
분야	통계학	계량경제학
계열	Regression model	Regression model
기원 연도	2001–2011	1978
창시자	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	Koenker & Bassett
유형	Bayesian semiparametric regression	Conditional quantile regression
원전	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.
별칭	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression	conditional quantile regression, regression quantiles, Kantil Regresyon
관련	6	5
요약	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.