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| Bayes-féle robusztus regresszió× | Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel× | |
|---|---|---|
| Tudományterület≠ | Statisztika | Ökonometria |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 1993 | 2019 |
| Megalkotó≠ | Geweke (1993); Gelman et al. (2013) | Wooldridge (textbook treatment); classical least squares |
| Típus≠ | Bayesian regression with heavy-tailed errors | Linear regression |
| Alapmű≠ | Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alternatív nevek | 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 |
| Kapcsolódó≠ | 6 | 5 |
| Összefoglaló≠ | 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). |
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