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| Regressió Quantílica Robusta× | Regressió per Mínims Quadrats Ordinàris (MQO)× | |
|---|---|---|
| Camp≠ | Estadística | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1993–1997 | 2019 |
| Autor original≠ | Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997) | Wooldridge (textbook treatment); classical least squares |
| Tipus≠ | Robust semiparametric regression | Linear regression |
| Font seminal≠ | Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Àlies | robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionats≠ | 6 | 5 |
| Resum≠ | Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions. | 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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