Vertaile menetelmiä

	Bayesiläinen robusti regressio	Bayesiläinen kvantiiliregressio
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1993	2001–2011
Kehittäjä	Geweke (1993); Gelman et al. (2013)	Kozumi & Kobayashi; building on Yu & Moyeed (2001)
Tyyppi	Bayesian regression with heavy-tailed errors	Bayesian semiparametric regression
Alkuperäislähde	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.	Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.
Rinnakkaisnimet	Bayesian heavy-tailed regression, Bayesian Student-t regression, robust Bayesian linear model, BRR	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression
Liittyvät	6	6
Tiivistelmä	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.	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.