Vertaile menetelmiä

	Bayesiläinen robusti regressio	OLS-regressio (Ordinary Least Squares)
Tieteenala	Tilastotiede	Ekonometria
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1993	2019
Kehittäjä	Geweke (1993); Gelman et al. (2013)	Wooldridge (textbook treatment); classical least squares
Tyyppi	Bayesian regression with heavy-tailed errors	Linear regression
Alkuperäislähde	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Rinnakkaisnimet	Bayesian heavy-tailed regression, Bayesian Student-t regression, robust Bayesian linear model, BRR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	6	5
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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).