방법 비교

	베이지안 분위수 회귀	베이즈 로버스트 회귀
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	2001–2011	1993
창시자	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	Geweke (1993); Gelman et al. (2013)
유형	Bayesian semiparametric regression	Bayesian regression with heavy-tailed errors
원전	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.
별칭	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
관련	6	6
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.	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.